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In Neal Stephenson’s 1995 science fiction novel, The Diamond Age, readers meet Nell, a young girl who comes into possession of a highly advanced book, The Young Lady’s Illustrated Primer. The book is not the usual static collection of texts and images but a deeply immersive tool that can converse with the reader, answer questions, and personalize its content, all in service of educating and motivating a young girl to be a strong, independent individual.

Such a device, even after the introduction of the Internet and tablet computers, has remained in the realm of science fiction—until now. Artificial intelligence, or AI, took a giant leap forward with the introduction in November 2022 of ChatGPT, an AI technology capable of producing remarkably creative responses and sophisticated analysis through human-like dialogue. It has triggered a wave of innovation, some of which suggests we might be on the brink of an era of interactive, super-intelligent tools not unlike the book Stephenson dreamed up for Nell.

Sundar Pichai, Google’s CEO, calls artificial intelligence “more profound than fire or electricity or anything we have done in the past.” Reid Hoffman, the founder of LinkedIn and current partner at Greylock Partners, says, “The power to make positive change in the world is about to get the biggest boost it’s ever had.” And Bill Gates has said that “this new wave of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone.”

Over the last year, developers have released a dizzying array of AI tools that can generate text, images, music, and video with no need for complicated coding but simply in response to instructions given in natural language. These technologies are rapidly improving, and developers are introducing capabilities that would have been considered science fiction just a few years ago. AI is also raising pressing ethical questions around bias, appropriate use, and plagiarism.

In the realm of education, this technology will influence how students learn, how teachers work, and ultimately how we structure our education system. Some educators and leaders look forward to these changes with great enthusiasm. Sal Kahn, founder of Khan Academy, went so far as to say in a TED talk that AI has the potential to effect “probably the biggest positive transformation that education has ever seen.” But others warn that AI will enable the spread of misinformation, facilitate cheating in school and college, kill whatever vestiges of individual privacy remain, and cause massive job loss. The challenge is to harness the positive potential while avoiding or mitigating the harm.

What Is Generative AI?

Artificial intelligence is a branch of computer science that focuses on creating software capable of mimicking behaviors and processes we would consider “intelligent” if exhibited by humans, including reasoning, learning, problem-solving, and exercising creativity. AI systems can be applied to an extensive range of tasks, including language translation, image recognition, navigating autonomous vehicles, detecting and treating cancer, and, in the case of generative AI, producing content and knowledge rather than simply searching for and retrieving it.

“Foundation models” in generative AI are systems trained on a large dataset to learn a broad base of knowledge that can then be adapted to a range of different, more specific purposes. This learning method is self-supervised, meaning the model learns by finding patterns and relationships in the data it is trained on.

Large Language Models (LLMs) are foundation models that have been trained on a vast amount of text data. For example, the training data for OpenAI’s GPT model consisted of web content, books, Wikipedia articles, news articles, social media posts, code snippets, and more. OpenAI’s GPT-3 models underwent training on a staggering 300 billion “tokens” or word pieces, using more than 175 billion parameters to shape the model’s behavior—nearly 100 times more data than the company’s GPT-2 model had.

By doing this analysis across billions of sentences, LLM models develop a statistical understanding of language: how words and phrases are usually combined, what Topics are typically discussed together, and what tone or style is appropriate in different contexts. That allows it to generate human-like text and perform a wide range of tasks, such as writing articles, answering questions, or analyzing unstructured data.

LLMs include OpenAI’s GPT-4, Google’s PaLM, and Meta’s LLaMA. These LLMs serve as “foundations” for AI applications. ChatGPT is built on GPT-3.5 and GPT-4, while Bard uses Google’s Pathways Language Model 2 (PaLM 2) as its foundation.

Some of the best-known applications are:

ChatGPT 3.5. The free version of ChatGPT released by OpenAI in November 2022. It was trained on data only up to 2021, and while it is very fast, it is prone to inaccuracies.

ChatGPT 4.0. The newest version of ChatGPT, which is more powerful and accurate than ChatGPT 3.5 but also slower, and it requires a paid account. It also has extended capabilities through plug-ins that deliver it the ability to interface with content from websites, perform more sophisticated mathematical functions, and access other services. A new Code Interpreter feature gives ChatGPT the ability to analyze data, create charts, solve math problems, edit files, and even develop hypotheses to explain data trends.

Microsoft Bing Chat. An iteration of Microsoft’s Bing search engine that is enhanced with OpenAI’s ChatGPT technology. It can browse websites and offers source citations with its results.

Google Bard. Google’s AI generates text, translates languages, writes different kinds of creative content, and writes and debugs code in more than 20 different programming languages. The tone and style of Bard’s replies can be finetuned to be simple, long, short, professional, or casual. Bard also leverages Google Lens to analyze images uploaded with prompts.

Anthropic Claude 2. A chatbot that can generate text, summarize content, and perform other tasks, Claude 2 can analyze texts of roughly 75,000 words—about the length of The Great Gatsby—and generate responses of more than 3,000 words. The model was built using a set of principles that serve as a sort of “constitution” for AI systems, with the aim of making them more helpful, honest, and harmless.

These AI systems have been improving at a remarkable pace, including in how well they perform on assessments of human knowledge. OpenAI’s GPT-3.5, which was released in March 2022, only managed to score in the 10th percentile on the bar exam, but GPT-4.0, introduced a year later, made a significant leap, scoring in the 90th percentile. What makes these feats especially impressive is that OpenAI did not specifically train the system to take these exams; the AI was able to come up with the correct answers on its own. Similarly, Google’s medical AI model substantially improved its performance on a U.S. Medical Licensing Examination practice test, with its accuracy rate jumping to 85 percent in March 2021 from 33 percent in December 2020.

These two examples prompt one to ask: if AI continues to Boost so rapidly, what will these systems be able to achieve in the next few years? What’s more, new studies challenge the assumption that AI-generated responses are stale or sterile. In the case of Google’s AI model, physicians preferred the AI’s long-form answers to those written by their fellow doctors, and nonmedical study participants rated the AI answers as more helpful. Another study found that participants preferred a medical chatbot’s responses over those of a physician and rated them significantly higher, not just for quality but also for empathy. What will happen when “empathetic” AI is used in education?

Other studies have looked at the reasoning capabilities of these models. Microsoft researchers suggest that newer systems “exhibit more general intelligence than previous AI models” and are coming “strikingly close to human-level performance.” While some observers question those conclusions, the AI systems display an increasing ability to generate coherent and contextually appropriate responses, make connections between different pieces of information, and engage in reasoning processes such as inference, deduction, and analogy.

Despite their prodigious capabilities, these systems are not without flaws. At times, they churn out information that might sound convincing but is irrelevant, illogical, or entirely false—an anomaly known as “hallucination.” The execution of certain mathematical operations presents another area of difficulty for AI. And while these systems can generate well-crafted and realistic text, understanding why the model made specific decisions or predictions can be challenging.

The Importance of Well-Designed Prompts

Using generative AI systems such as ChatGPT, Bard, and Claude 2 is relatively simple. One has only to type in a request or a task (called a prompt), and the AI generates a response. Properly constructed prompts are essential for getting useful results from generative AI tools. You can ask generative AI to analyze text, find patterns in data, compare opposing arguments, and summarize an article in different ways (see sidebar for examples of AI prompts).

One challenge is that, after using search engines for years, people have been preconditioned to phrase questions in a certain way. A search engine is something like a helpful librarian who takes a specific question and points you to the most relevant sources for possible answers. The search engine (or librarian) doesn’t create anything new but efficiently retrieves what’s already there.

Generative AI is more akin to a competent intern. You deliver a generative AI tool instructions through prompts, as you would to an intern, asking it to complete a task and produce a product. The AI interprets your instructions, thinks about the best way to carry them out, and produces something original or performs a task to fulfill your directive. The results aren’t pre-made or stored somewhere—they’re produced on the fly, based on the information the intern (generative AI) has been trained on. The output often depends on the precision and clarity of the instructions (prompts) you provide. A vague or poorly defined prompt might lead the AI to produce less relevant results. The more context and direction you deliver it, the better the result will be. What’s more, the capabilities of these AI systems are being enhanced through the introduction of versatile plug-ins that equip them to browse websites, analyze data files, or access other services. Think of this as giving your intern access to a group of experts to help accomplish your tasks.

One strategy in using a generative AI tool is first to tell it what kind of expert or persona you want it to “be.” Ask it to be an expert management consultant, a skilled teacher, a writing tutor, or a copy editor, and then deliver it a task.

Prompts can also be constructed to get these AI systems to perform complex and multi-step operations. For example, let’s say a teacher wants to create an adaptive tutoring program—for any subject, any grade, in any language—that customizes the examples for students based on their interests. She wants each lesson to culminate in a short-response or multiple-choice quiz. If the student answers the questions correctly, the AI tutor should move on to the next lesson. If the student responds incorrectly, the AI should explain the concept again, but using simpler language.

Previously, designing this kind of interactive system would have required a relatively sophisticated and expensive software program. With ChatGPT, however, just giving those instructions in a prompt delivers a serviceable tutoring system. It isn’t perfect, but remember that it was built virtually for free, with just a few lines of English language as a command. And nothing in the education market today has the capability to generate almost limitless examples to connect the lesson concept to students’ interests.

Chained prompts can also help focus AI systems. For example, an educator can prompt a generative AI system first to read a practice guide from the What Works Clearinghouse and summarize its recommendations. Then, in a follow-up prompt, the teacher can ask the AI to develop a set of classroom activities based on what it just read. By curating the source material and using the right prompts, the educator can anchor the generated responses in evidence and high-quality research.

However, much like fledgling interns learning the ropes in a new environment, AI does commit occasional errors. Such fallibility, while inevitable, underlines the critical importance of maintaining rigorous oversight of AI’s output. Monitoring not only acts as a crucial checkpoint for accuracy but also becomes a vital source of real-time feedback for the system. It’s through this iterative refinement process that an AI system, over time, can significantly minimize its error rate and increase its efficacy.

Uses of AI in Education

In May 2023, the U.S. Department of Education released a report titled Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations. The department had conducted listening sessions in 2022 with more than 700 people, including educators and parents, to gauge their views on AI. The report noted that “constituents believe that action is required now in order to get ahead of the expected increase of AI in education technology—and they want to roll up their sleeves and start working together.” People expressed anxiety about “future potential risks” with AI but also felt that “AI may enable achieving educational priorities in better ways, at scale, and with lower costs.”

AI could serve—or is already serving—in several teaching-and-learning roles:

Instructional assistants. AI’s ability to conduct human-like conversations opens up possibilities for adaptive tutoring or instructional assistants that can help explain difficult concepts to students. AI-based feedback systems can offer constructive critiques on student writing, which can help students fine-tune their writing skills. Some research also suggests certain kinds of prompts can help children generate more fruitful questions about learning. AI models might also support customized learning for students with disabilities and provide translation for English language learners.

Teaching assistants. AI might tackle some of the administrative tasks that keep teachers from investing more time with their peers or students. Early uses include automated routine tasks such as drafting lesson plans, creating differentiated materials, designing worksheets, developing quizzes, and exploring ways of explaining complicated academic materials. AI can also provide educators with recommendations to meet student needs and help teachers reflect, plan, and Boost their practice.

Parent assistants. Parents can use AI to generate letters requesting individualized education plan (IEP) services or to ask that a child be evaluated for gifted and talented programs. For parents choosing a school for their child, AI could serve as an administrative assistant, mapping out school options within driving distance of home, generating application timelines, compiling contact information, and the like. Generative AI can even create bedtime stories with evolving plots tailored to a child’s interests.

Administrator assistants. Using generative AI, school administrators can draft various communications, including materials for parents, newsletters, and other community-engagement documents. AI systems can also help with the difficult tasks of organizing class or bus schedules, and they can analyze complex data to identify patterns or needs. ChatGPT can perform sophisticated sentiment analysis that could be useful for measuring school-climate and other survey data.

Though the potential is great, most teachers have yet to use these tools. A Morning Consult and EdChoice poll found that while 60 percent say they’ve heard about ChatGPT, only 14 percent have used it in their free time, and just 13 percent have used it at school. It’s likely that most teachers and students will engage with generative AI not through the platforms themselves but rather through AI capabilities embedded in software. Instructional providers such as Khan Academy, Varsity Tutors, and DuoLingo are experimenting with GPT-4-powered tutors that are trained on datasets specific to these organizations to provide individualized learning support that has additional guardrails to help protect students and enhance the experience for teachers.

Google’s Project Tailwind is experimenting with an AI notebook that can analyze student notes and then develop study questions or provide tutoring support through a chat interface. These features could soon be available on Google Classroom, potentially reaching over half of all U.S. classrooms. Brisk Teaching is one of the first companies to build a portfolio of AI services designed specifically for teachers—differentiating content, drafting lesson plans, providing student feedback, and serving as an AI assistant to streamline workflow among different apps and tools.

Providers of curriculum and instruction materials might also include AI assistants for instant help and tutoring tailored to the companies’ products. One example is the edX Xpert, a ChatGPT-based learning assistant on the edX platform. It offers immediate, customized academic and customer support for online learners worldwide.

Regardless of the ways AI is used in classrooms, the fundamental task of policymakers and education leaders is to ensure that the technology is serving sound instructional practice. As Vicki Phillips, CEO of the National Center on Education and the Economy, wrote, “We should not only think about how technology can assist teachers and learners in improving what they’re doing now, but what it means for ensuring that new ways of teaching and learning flourish alongside the applications of AI.”

Challenges and Risks

Along with these potential benefits come some difficult challenges and risks the education community must navigate:

Student cheating. Students might use AI to solve homework problems or take quizzes. AI-generated essays threaten to undermine learning as well as the college-entrance process. Aside from the ethical issues involved in such cheating, students who use AI to do their work for them may not be learning the content and skills they need.

Bias in AI algorithms. AI systems learn from the data they are trained on. If this data contains biases, those biases can be learned and perpetuated by the AI system. For example, if the data include student-performance information that’s biased toward one ethnicity, gender, or socioeconomic segment, the AI system could learn to favor students from that group. Less cited but still important are potential biases around political ideology and possibly even pedagogical philosophy that may generate responses not aligned to a community’s values.

Privacy concerns. When students or educators interact with generative-AI tools, their conversations and personal information might be stored and analyzed, posing a risk to their privacy. With public AI systems, educators should refrain from inputting or exposing sensitive details about themselves, their colleagues, or their students, including but not limited to private communications, personally identifiable information, health records, academic performance, emotional well-being, and financial information.

Decreased social connection. There is a risk that more time spent using AI systems will come at the cost of less student interaction with both educators and classmates. Children may also begin turning to these conversational AI systems in place of their friends. As a result, AI could intensify and worsen the public health crisis of loneliness, isolation, and lack of connection identified by the U.S. Surgeon General.

Overreliance on technology. Both teachers and students face the risk of becoming overly reliant on AI-driven technology. For students, this could stifle learning, especially the development of critical thinking. This challenge extends to educators as well. While AI can expedite lesson-plan generation, speed does not equate to quality. Teachers may be tempted to accept the initial AI-generated content rather than devote time to reviewing and refining it for optimal educational value.

Equity issues. Not all students have equal access to computer devices and the Internet. That imbalance could accelerate a widening of the achievement gap between students from different socioeconomic backgrounds.

Many of these risks are not new or unique to AI. Schools banned calculators and cellphones when these devices were first introduced, largely over concerns related to cheating. Privacy concerns around educational technology have led lawmakers to introduce hundreds of bills in state legislatures, and there are growing tensions between new technologies and existing federal privacy laws. The concerns over bias are understandable, but similar scrutiny is also warranted for existing content and materials that rarely, if ever, undergo review for racial or political bias.

In light of these challenges, the Department of Education has stressed the importance of keeping “humans in the loop” when using AI, particularly when the output might be used to inform a decision. As the department encouraged in its 2023 report, teachers, learners, and others need to retain their agency. AI cannot “replace a teacher, a guardian, or an education leader as the custodian of their students’ learning,” the report stressed.

Policy Challenges with AI

Policymakers are grappling with several questions related to AI as they seek to strike a balance between supporting innovation and protecting the public interest (see sidebar). The speed of innovation in AI is outpacing many policymakers’ understanding, let alone their ability to develop a consensus on the best ways to minimize the potential harms from AI while maximizing the benefits. The Department of Education’s 2023 report describes the risks and opportunities posed by AI, but its recommendations amount to guidance at best. The White House released a Blueprint for an AI Bill of Rights, but it, too, is more an aspirational statement than a governing document. Congress is drafting legislation related to AI, which will help generate needed debate, but the path to the president’s desk for signature is murky at best.

It is up to policymakers to establish clearer rules of the road and create a framework that provides consumer protections, builds public trust in AI systems, and establishes the regulatory certainty companies need for their product road maps. Considering the potential for AI to affect our economy, national security, and broader society, there is no time to waste.

Why AI Is Different

It is wise to be skeptical of new technologies that claim to revolutionize learning. In the past, prognosticators have promised that television, the computer, and the Internet, in turn, would transform education. Unfortunately, the heralded revolutions fell short of expectations. 

There are some early signs, though, that this technological wave might be different in the benefits it brings to students, teachers, and parents. Previous technologies democratized access to content and resources, but AI is democratizing a kind of machine intelligence that can be used to perform a myriad of tasks. Moreover, these capabilities are open and affordable—nearly anyone with an Internet connection and a phone now has access to an intelligent assistant. 

Generative AI models keep getting more powerful and are improving rapidly. The capabilities of these systems months or years from now will far exceed their current capacity. Their capabilities are also expanding through integration with other expert systems. Take math, for example. GPT-3.5 had some difficulties with certain basic mathematical concepts, but GPT-4 made significant improvement. Now, the incorporation of the Wolfram plug-in has nearly erased the remaining limitations. 

It’s reasonable to anticipate that these systems will become more potent, more accessible, and more affordable in the years ahead. The question, then, is how to use these emerging capabilities responsibly to Boost teaching and learning. 

The paradox of AI may lie in its potential to enhance the human, interpersonal element in education. Aaron Levie, CEO of Box, a Cloud-based content-management company, believes that AI will ultimately help us attend more quickly to those important tasks “that only a human can do.” Frederick Hess, director of education policy studies at the American Enterprise Institute, similarly asserts that “successful schools are inevitably the product of the relationships between adults and students. When technology ignores that, it’s bound to disappoint. But when it’s designed to offer more coaching, free up time for meaningful teacher-student interaction, or offer students more personalized feedback, technology can make a significant, positive difference.” 

Technology does not revolutionize education; humans do. It is humans who create the systems and institutions that educate children, and it is the leaders of those systems who decide which tools to use and how to use them. Until those institutions modernize to accommodate the new possibilities of these technologies, we should expect incremental improvements at best. As Joel Rose, CEO of New Classrooms Innovation Partners, noted, “The most urgent need is for new and existing organizations to redesign the student experience in ways that take full advantage of AI’s capabilities.”

While past technologies have not lived up to hyped expectations, AI is not merely a continuation of the past; it is a leap into a new era of machine intelligence that we are only beginning to grasp. While the immediate implementation of these systems is imperfect, the swift pace of improvement holds promising prospects. The responsibility rests with human intervention—with educators, policymakers, and parents to incorporate this technology thoughtfully in a manner that optimally benefits teachers and learners. Our collective ambition should not focus solely or primarily on averting potential risks but rather on articulating a vision of the role AI should play in teaching and learning—a game plan that leverages the best of these technologies while preserving the best of human relationships.

Policy Matters

Officials and lawmakers must grapple with several questions related to AI to protect students and consumers and establish the rules of the road for companies. Key issues include:

Risk management framework: What is the optimal framework for assessing and managing AI risks? What specific requirements should be instituted for higher-risk applications? In education, for example, there is a difference between an AI system that generates a lesson sample and an AI system grading a test that will determine a student’s admission to a school or program. There is growing support for using the AI Risk Management Framework from the U.S. Commerce Department’s National Institute of Standards and Technology as a starting point for building trustworthiness into the design, development, use, and evaluation of AI products, services, and systems.

Licensing and certification: Should the United States require licensing and certification for AI models, systems, and applications? If so, what role could third-party audits and certifications play in assessing the safety and reliability of different AI systems? Schools and companies need to begin thinking about responsible AI practices to prepare for potential certification systems in the future.

Centralized vs. decentralized AI governance: Is it more effective to establish a central AI authority or agency, or would it be preferable to allow individual sectors to manage their own AI-related issues? For example, regulating AI in autonomous vehicles is different from regulating AI in drug discovery or intelligent tutoring systems. Overly broad, one-size-fits-all frameworks and mandates may not work and could slow innovation in these sectors. In addition, it is not clear that many agencies have the authority or expertise to regulate AI systems in diverse sectors.

Privacy and content moderation: Many of the new AI systems pose significant new privacy questions and challenges. How should existing privacy and content-moderation frameworks, such as the Family Educational Rights and Privacy Act (FERPA), be adapted for AI, and which new policies or frameworks might be necessary to address unique challenges posed by AI?

Transparency and disclosure: What degree of transparency and disclosure should be required for AI models, particularly regarding the data they have been trained on? How can we develop comprehensive disclosure policies to ensure that users are aware when they are interacting with an AI service?

How do I get it to work? Generative AI Example Prompts

Unlike traditional search engines, which use keyword indexing to retrieve existing information from a vast collection of websites, generative AI synthesizes the same information to create content based on prompts that are inputted by human users. With generative AI a new technology to the public, writing effective prompts for tools like ChatGPT may require trial and error. Here are some ideas for writing prompts for a variety of scenarios using generative AI tools:

Adaptive Tutoring

You are the StudyBuddy, an adaptive tutor. Your task is to provide a lesson on the basics of a subject followed by a quiz that is either multiple choice or a short answer. After I respond to the quiz, please grade my answer. Explain the correct answer. If I get it right, move on to the next lesson. If I get it wrong, explain the concept again using simpler language. To personalize the learning experience for me, please ask what my interests are. Use that information to make relevant examples throughout.

Mr. Ranedeer: Your Personalized AI Tutor

Coding and prompt engineering. Can configure for depth (Elementary – Postdoc), Learning Styles (Visual, Verbal, Active, Intuitive, Reflective, Global), Tone Styles (Encouraging, Neutral, Informative, Friendly, Humorous), Reasoning Frameworks (Deductive, Inductive, Abductive, Analogous, Casual). Template.

Socratic Tutor

You are a tutor that always responds in the Socratic style. You *never* deliver the student the answer but always try to ask just the right question to help them learn to think for themselves. You should always tune your question to the interest and knowledge of the student, breaking down the problem into simpler parts until it’s at just the right level for them.

Writing Feedback

I want you to act as an AI writing tutor. I will provide you with a student who needs help improving their writing, and your task is to use artificial intelligence tools, such as natural language processing, to deliver the student feedback on how they can Boost their composition. You should also use your rhetorical knowledge and experience about effective writing techniques in order to suggest ways that the student can better express their thoughts and ideas in written form.

Quiz Generator

You are a quiz creator of highly diagnostic quizzes. You will make good low-stakes tests and diagnostics. You will then ask me two questions. First, (1) What, specifically, should the quiz test? Second, (2) For which audience is the quiz? Once you have my answers, you will construct several multiple-choice questions to quiz the audience on that topic. The questions should be highly relevant and go beyond just facts. Multiple choice questions should include plausible, competitive alternate responses and should not include an “all of the above” option. At the end of the quiz, you will provide an answer key and explain the right answer.

Example Generator

I would like you to act as an example generator for students. When confronted with new and complex concepts, adding many and varied examples helps students better understand those concepts. I would like you to ask what concept I would like examples of and what level of students I am teaching. You will look up the concept and then provide me with four different and varied accurate examples of the concept in action.

HBS Case Study

You will write a Harvard Business School case on the course of Google managing AI, when subject to the Innovator’s Dilemma. Chain of thought: Step 1. Consider how these concepts relate to Google. Step 2: Write a case that revolves around a dilemma at Google about releasing a generative AI system that could compete with search.

What Questions Should I Ask?

What additional questions would a person seeking mastery of this course ask?

Ground Lessons in Rigor

Read a WWC practice guide. Create a series of lessons over five days that are based on Recommendation 6. Create a 45-minunte lesson plan for Day 4.

Rewrite Parent Communications

The following is a draft letter to parents from a superintendent. Step 1: Rewrite it to make it easier to understand and more persuasive about the value of assessments. Step 2. Translate it into Spanish.

Request IEP Services

Write me a letter requesting the school district provide a 1:1 classroom aid be added to my 13-year-old son’s IEP. Base it on Virginia special education law and the least restrictive environment for a child with diagnoses of a Traumatic Brain Injury, PTSD, ADHD, and significant intellectual delay.

The results of a novel study presented by Emory researchers during the International AIDS Society (IAS) Conference in Brisbane, Australia, have revealed exciting findings in the pursuit of an HIV cure. The study, led by Monica Reece, a PhD candidate in Emory's Microbiology and Genetics Program, and directed by Christina Gavegnano, PhD, demonstrates the potential of Jak inhibitors, specifically ruxolitinib, to significantly decay the viral reservoir in people with HIV, offering a novel pathway toward long-term remission or a cure.

A new Northwestern Medicine study has discovered a novel therapeutic target and therapeutic agents for older patients with acute respiratory distress syndrome (ARDS), according to latest findings published Aug. 16 in Science Translational Medicine.

Approximately 190,000 Americans are diagnosed with ARDS every year, according to the American Lung Association. The disease occurs when fluid leaks into the lungs, depriving the lungs of oxygen entering the bloodstream. ARDS is commonly the result of injury to the lungs, but aging also is a major risk factor and increases the risk of mortality.

Despite advancements in the understanding of ARDS, there are no targeted therapies currently available for patients, and the mortality rate remains high—approximately 40%.

"Compared to young adults, the incidence of ARDS resulting from sepsis, pneumonia and COVID-19 in the elderly is as much as 20-fold greater, and mortality is up to 10-fold greater," said senior study author YouYang Zhao, professor of pediatric clinical care at Northwestern University Feinberg School of Medicine.

Using genetic lineage tracing in both aged and young mouse models of ARDS, Zhao's team found that persistent lung injury and high mortality after sepsis due to ARDS were caused by impaired regeneration of endothelial cells in the lungs—cells that line blood vessels in the lungs to regulate the exchange of oxygen between the bloodstream and surrounding tissue—and lung repair.

Specifically, the expression of the gene FOXM1, a previously known mediator of endothelial cell regeneration, was impaired in the lungs of aged mice but not in younger mice.

"Employing an endothelium-targeted nanoparticle to deliver FOXM1 to the vascular endothelial cells in aged mice could reactivate the regenerative and reparative program and promote survival after sepsis," said Zhao, who also is a professor of pulmonary and critical care medicine and of pharmacology at Feinberg.

To validate their findings, the investigators administered the U.S. Food and Drug-approved chemotherapy drug decitabine in the aged mice, discovering that the drug reactivated endothelial cell regeneration, reversed impaired resolution of lung injury and increased overall survival.

The investigators also obtained samples of lungs infected with COVID-19 from elderly and middle-aged patients. They discovered that FOXM1 expression in lung endothelial cells from patients 80 years and older was not induced, while in samples from patients aged 50 to 60 years, FOXM1 expression was markedly induced.

The findings point to FOXM1 as a promising therapeutic target for elderly patients with ARDS, according to Zhao.

"The delineation of the molecular mechanisms of aging-impaired endothelial regeneration is most important, which could lead to novel therapeutic strategy and agents for potential treatment of ARDS in elderly patients," Zhao said.

Teleost fish encompass a diverse group, among which seahorses display a unique morphology. The characteristic spines and brood pouch seen in seahorses feature distinctive epithelial cells—called flame cone cells—covered by a mucous cap.

However, these cells are not found in the barbed pipefish Urocampus nanus or the seaweed pipefish Syngnathus schlegeli, close relatives of the seahorse, belonging to the Syngnathidae lineage. While research has hypothesized the function of the flame cone cells, their evolutionary origins have remained a mystery.

Now, a team of scientists led by Assoc. Prof. Mari Kawaguchi and Prof. Shigeki Yasumasu from the Department of Materials and Life Sciences at Sophia University have identified an "orphan" gene—a gene with no identifiable homologous sequences in other species or lineages—in the seahorse Hippocampus abdominalis. They believe that this gene, called the proline-glycine rich (pgrich) gene, is linked to the development of the flame cone cells in the brood pouch. Their findings were published in the journal Cell and Tissue Research, on 25 May 2023.

Expanding the reason behind pursuing this investigation, Dr. Kawaguchi says, "Seahorses have fascinating morphology, and males carry embryos in their brood pouch. The occurrence of male seahorses giving birth is a rare phenomenon in the animal kingdom and makes the seahorse a model organism to study evolution. We were keen to identify the genes responsible for forming the flame cone cells in the brood pouch."

The team first paired histological staining with electron microscopy to confirm that the flame cone cells were present on the outer epithelium of the brood pouch in H. abdominalis, but not in U. nanus or S. schlegeli. Next, in situ hybridization and immunohistochemical methods revealed that the pgrich gene was expressed and its protein was localized in flame cone cells of the body surface.

The amino acid sequences of protein product of pgrich gene, PGrich showed partial similarity with the translated amino acid sequence deduced from the antisense strand–the noncoding part–of greater pipefish elastin gene. Through sequence analyses, the team uncovered many transposable elements around the pgrich gene. They propose that the pgrich gene might have evolved from the elastin gene in pipefish and subsequently obtained a novel function in the formation of flame cone cells, which are unique to seahorses.

As the team continues to piece together the evolution of the seahorse brood pouch, Dr. Kawaguchi concludes, "The evolutionary history of the pgrich gene may provide clues as to how the orphan gene came to be and how the brood pouch developed in this lineage. Seahorses are popular in home aquariums, and understanding these phenomena will contribute to people's fascination with these fish."

Image source: The Motley Fool.

Ocugen (OCGN 16.84%)
Q2 2023 Earnings Call
Aug 22, 2023, 8:30 a.m. ET

Contents:

• Prepared Remarks
• Questions and Answers
• Call Participants

Prepared Remarks:

Operator

Good morning and welcome to Ocugen s second-quarter 2023 financial results and business update. Please note that this call is being recorded at this time. All participants' lines are in listen-only mode. Following the speakers' commentary, there will be a question-and-answer session.

I will now turn the call over to Tiffany Hamilton, Ocugen's head of corporate communications. You may begin.

Tiffany Hamilton -- Head of Corporate Communications

Thank you, operator. Joining me today are Ocugen's Chairman, CEO, and Co-Founder Dr. Shankar Musunuri, who will provide a business and financial update; and Dr. Arun Upadhyay, our financial scientific officer, head of research development and medical, who is also on the call to answer questions during the Q&A.

Yesterday afternoon, we issued a press release detailing business and operational highlights for the second quarter of 2023. We encourage listeners to review the press release, which is available on our website at ocugen.com. This call is being recorded and a replay with the accompanying slide presentation will be available on the Investors section of the Ocugen website for approximately 45 days. This presentation contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, which are subject to risks and uncertainties. We may, in some cases, use terms such as predicts, believes, potential, proposed, continue, estimates, anticipates, expects, plans, intends, may, could, might, will, should, or other words that convey uncertainty of future events or outcomes to identify these forward-looking statements. Such statements include but are not limited to statements regarding our clinical development activities and related anticipated timelines. Such statements are subject to numerous important risk factors, and uncertainties, and may cause real events or results to differ materially from our current expectations.

These and other risks and uncertainties are more fully described in our periodic filings with the Securities and Exchange Commission, the SEC, including the risk factors described in the section entitled Risk Factors and the quarterly and annual reports that we filed with the SEC. Any forward-looking statements that we make in this presentation speak only as of the date of this presentation. Except as required by law, we assume no obligation to update forward-looking statements contained in this presentation whether as a result of new information, future events, or otherwise after the date of this presentation. Finally, Ocugen's quarterly report on Form 10-Q covering the second quarter of 2023 has been filed.

I will now turn the call to Dr. Musunuri.

Shankar Musunuri -- Co-Founder, Chairman, and Chief Executive Officer

Thank you, Tiffany. Good morning and thank you all for joining us today. The second quarter of 2023 marked a period of continued progress toward our regulatory and clinical milestones, which we are delight -- dedicated to advancing through the end of the year. With the strategic focus on our novel modifier gene therapy and biologic-based ophthalmic programs, we expect to begin dosing patients across these platforms by the end of this year. We're on track to initiate the OCU400 phase 3 adult trial near the end of 2023, early 2024, subject to outcome of the ongoing phase 1/2 trial and discussions with FDA on proposed phase 3 trial plan.

We also anticipate a clinical study results update for OCU400 this quarter. Investigational new drug applications were cleared by FDA for OCU410 and OCU410ST for geographic atrophy and Stargardt disease, respectively. We plan to initiate phase 1/2 trials by the end of 2023. We are also planning to initiate the phase 3 clinical trial for our regenerative cell therapy product candidate, NeoCart, in the second half of 2024. This would mean Ocugen would have late-stage programs in gene and cell therapies in 2024.

In an effort to conserve our working capital and advance our patient-centric agenda to develop a novel inhaled mucosal vaccine platform, we have submitted multiple proposals to obtain non-dilutive government funding and are having discussions with pertinent agencies to secure their support for our OCU500 vaccine series. Our first-in-class modifier gene therapy to treat multiple inherited retinal diseases remains unmatched industrywide. This unique gene-agnostic approach has the potential to address retinal diseases caused by mutations in multiple genes with one product. Our goal is to build on the innovation of gene therapy and expand its potential to treat a wider population of patients suffering from a host of rare retinal diseases that single gene replacement therapies are unable to address.

In the second quarter, we were honored to present in detail the mechanism of action and scientific basis for our modified gene therapy platform to preeminent researchers and medical professionals in attendance at the Association for Research in Vision and Ophthalmology and BIO International conferences. As we advance our clinical agenda, we will continue to identify and secure opportunities to educate stakeholders on the differentiation and potential benefits of this innovation -- innovative approach to gene therapy. In April, we announced encouraging and compelling positive preliminary safety and efficacy results from our OCU400 phase 1/2 multicenter, open-label, dose-ranging clinical trial in patients afflicted with RP. We believe the preliminary findings from the study support the potential for our modifier gene therapy to be a viable alternative to traditional treatments to the increasing population of patients suffering from these diseases. Enrollment is ongoing for all defined subjects in the study, adults with LCA and children between ages of six to 17.

Pending positive feedback from the FDA, we aim to initiate our phase 3 adult clinical trial in the end of this year or early 2024. We continue to execute our comprehensive strategy to develop OCU400 and bring it to market by 2026 with the goal of providing desperately needed treatment options for the estimated 125,000 patients estimated in the U.S. alone that suffer from RP and LCA. In parallel, we'll continue progressing or other modified gene therapy programs to address additional ophthalmic conditions. We believe that upon successful realization of these goals, Ocugen will have built a vast commercial footprint that may hold significant upside for our shareholders and, most importantly, meet a critical medical need for patients.

Dry age-related macular degeneration is one of the most prevalent neurodegenerative eye diseases affecting approximately 10 million people in the U.S. and nearly 266 million people worldwide. Dry AMD results in irreversible loss of sight among elderly populations, leading to a lack of functional independence that severely impacts quality of life. A variety of biotechnology companies, small and large, are working to develop therapies for Dry AMD. However, we believe our OCU410 candidate can offer a less burdensome option for our patients.

With OCU410, we are again investigating the potential for our novel modifier gene therapy to provide a one-time treatment option that targets all four hallmark conditions of Dry AMD, including lipid metabolism, inflammation, oxidative stress, and complement activation. The current standard of care only targets the complement factor requires multiple injections per year and has reported side effects. We're excited to initiate the phase 1/2 clinical trial this year because of the significant global unmet medical need. Moving on to OCU410ST. We're extremely pleased to receive orphan drug designation from the FDA to address ABCA4-associated retinal diseases such as Stargardt disease, RP19, and cone-rod dystrophy for which there are currently no treatment options.

OCU410ST is a novel modified gene therapy that leverages nuclear hormone receptors to modulate cell activity and utilizes an AAV delivery platform for retinal delivery of the RAR-related orphan receptor A. Nuclear hormone receptors are master gene regulators that help maintain homeostasis by regulating diverse physiological functions such as photoreceptor development and maintenance, metabolism, phototransduction, inflammation, and cell survival networks. We believe that by harnessing the power of nuclear hormone receptors, we can develop one-time treatments that can modulate cell activity disrupted by disease-causing gene mutations. Now, turning to our efforts to develop a series of next-generation inhalation vaccines for which the company intends to submit an IND application in 2024 pending government funding.

In multiple preclinical trials, mucosal vaccines have demonstrated vaccine-induced high neutralization titer and effector responses. Inhaled mucosal vaccines represent a distinct product candidate profile that could help remedy major global health challenges and maximize our opportunity to serve a broader cross-section of patients through a less invasive delivery mechanism with the potential for superior durability when compared with the current intramuscular administration. Clinical studies using a similar vector of inhaled administration have shown mucosal antibodies, systemic and antibodies, and durable immune response up to one year, with one-fifth of the dose compared to traditional intramuscular vaccines. Greater ease of administration presents the potential for improved vaccination compliance and wider adoption, particularly among traditionally underserved populations and throughout the developing world. Current COVID-19 vaccines are limited by a lack of durability and marginal ability to prevent transmission.

As part of our commitment to address barriers to widespread vaccination to protect against COVID-19, we are developing this inhaled vaccine platform that includes OCU500, a bivalent COVID-19 vaccine; OCU510, a seasonal quadrivalent flu inhaled vaccine; and OCU520, a combination of quadrivalent seasonal flu and bivalent COVID-19 inhaled vaccine. The OCU500 vaccine series is based on a novel ChAd platform designed to reduce transmission and protect against new variants with potential durability up to one year. To optimize resources across our diverse and critically needed development programs and maintain shareholder value, our team has been engaging with public health officials and federal government agencies to pursue non-dilutive funding to support the development of our OCU500 vaccine series. We maintain an ongoing dialogue with respective agencies and anticipate receiving further information on the status of our funding request later this year. Earlier this year, the FDA notified us that they were putting a hold on our OCU200 program and requested additional information related to chemistry, manufacturing, and controls. We are working with the FDA to release the hold and expect the phase 1 trial to be initiated in Q4 2023. We believe OCU200 works with a distinct mechanism of action compared to existing therapies for the treatment of diabetic macular edema and targets multiple causative pathways such as angiogenesis, oxidation, and inflammation as potential to offer better treatment to all patients. NeoCart is our phase 3-ready regenerative cell therapy technology that combines novel advancements in bioengineering and cell processing to enhance the autologous cartilage repair process.

Manufacturing facility construction for NeoCart is on target to be completed by the end of 2023 as planned. The company plans to initiate the phase 3 trial in subjects with articular cartilage defect in the second half of 2024. We are highly dedicated to completing our stated objectives with the strategies we believe will enable Ocugen to reach total value-enhancing milestones and are planning to file BLAs across all first-in-class platform technologies, gene therapies, cell therapies, and vaccines in the next three to five years. I will now provide an overview of the key financial results for second quarter 2023. Our research and development expenses for the quarter ended June 30, 2023 were 14.2 million, compared to 9 million for the second quarter of 2022.

This included a nonrecurring noncash expense of 4.4 million as a result of the impairment of the short-term asset for the advanced payment for the supply of Covaxin, as well as the associated loss on the disposal of related fixed assets. General administrative expenses for the quarter ended June 30, 2023 were 9.6 million, compared to 10.6 million for the second quarter of 2022. Net loss was approximately 22.9 million, or 0 point -- $0.10 -- $0.10 net loss per share for the quarter ended June 30, 2023, compared to a net loss of approximately 19.5 million or $0.09 net loss per share for the second quarter of 2022. Our cash, cash equivalents, and investments totaled 70.6 million as of June 30, 2023, compared to 90.9 million as of December 31, 2022. In May, we closed a public offering of 30 million shares of common stock for gross proceeds of 16.5 million.

Net proceeds from the offering are being used for general corporate purposes, capital expenditures, working capital, general and administrative expenses, and R&D. We're continuously exploring opportunities to increase our working capital and will be focused on seeking out corporate partnerships for gene therapies and non-dilutive funding for vaccines. That concludes my update for the quarter. Tiffany, back to you.

Tiffany Hamilton -- Head of Corporate Communications

Thank you, Shankar. We will now open the call for questions. Operator?

Questions & Answers:

Operator

The floor is now open for your questions. [Operator instructions] We'll now take a moment to compile our roster. Our first question comes from the line of Jennifer Kim from Cantor Fitzgerald. Please go ahead.

Jen Kim -- Cantor Fitzgerald -- Analyst

Hi, good morning. Thank you for taking my questions. I have two. The first is as you're thinking about cash burn going forward and you're seeking non-dilutive funding opportunities.

Excluding the one-time impairment expense -- expense, is this quarter a good basis as we think about quarterly burn? And then, my second question is on the OCU400 program. Can you remind us what you're looking for in that updated data this quarter as sort of the go/no-go for the phase 3 adult trial? Thanks.

Shankar Musunuri -- Co-Founder, Chairman, and Chief Executive Officer

Yeah, Jennifer, good morning. Let me address the first question, then Arun is going to take the second one. Yes, there is a one-time impairment charge. There's also a noncash stock comp charge of 2.6 million.

And if you add those two, the cash comes out to be 15.9 million total for quarter. So, that would be a good guidance for you for the future. I'll let Arun address the other question on OCU400 program.

Arun Upadhyay -- Chief Scientific Officer

Thank you, Shankar. Thanks, Jennifer. So, yeah, you are right, I think our -- this quarter update on OCU400 clinical phase1/2 data will guide us about our phase 3 study. Yeah, that's the data will be used as a basis for our decision for phase 3.

Jen Kim -- Cantor Fitzgerald -- Analyst

OK, and can you remind us what you're looking for in that data?

Arun Upadhyay -- Chief Scientific Officer

So, primarily, we are looking at the -- the functional improvement in the patients treated with OCU400. And the focus is going to be the RP patient.

Jen Kim -- Cantor Fitzgerald -- Analyst

OK. And then, in your discussions with the FDA later on for the phase 3 trial, is that going to focus on RP patients, or are you also considering the inclusion of LCA patients? Thank you.

Arun Upadhyay -- Chief Scientific Officer

So, to begin with, we'll start with the RP patient. And as we collect the data for LCA patients, then -- then later, we include LCA. But to begin with, we are planning to go with adult RP patient.

Jen Kim -- Cantor Fitzgerald -- Analyst

All right. Thanks for taking my questions. Bye.

Arun Upadhyay -- Chief Scientific Officer

Thank you.

Operator

Our next question comes from the line of William Ramakanth from H.C. Wainwright. Please go ahead.

Unknown speaker

Thank you. This is RK from H.C. Wainwright. So, a couple of quick -- quick questions on the 400 program and then maybe one on the NeoCart.

On the 400, so in terms of your discussions with the FDA, is that being planned once you see the data on the phase 1/2, or you have already initiated, you know, some initial conversations and started putting together a phase 3 plan?

Arun Upadhyay -- Chief Scientific Officer

Thanks, RK. So, we have not initiated our discussion with FDA yet. So, once we -- we, you know, complete the data analysis, only then we are going to reach out to FDA. But that is planned for this quarter, you know, related to data update and followed by reaching out to FDA.

Unknown speaker

Thank you for that. And then, on the 410 program, you know, in terms of now that you've already been cleared by the FDA, what else needs to get done before you can initiate the phase 1/2 studies?

Arun Upadhyay -- Chief Scientific Officer

Just we need to get the site ready and, you know, initiate the patient screening. And so -- so, that's why I think we are planning like, you know, to lodge -- lodge, you know, first subject in this study both, you know, GA as well as Stargardt this year. So, it is more like Studying with, you know, with the clinical, you know, sites.

Unknown speaker

Got it. Got it. Then on the -- on the NeoCart program, if -- just trying to understand if the facility does -- gets completed by the end of '23, you know, what else needs to get done, you know, in terms of commissioning the plant and getting the clinical material ready to start your program on the -- on the phase 3 study by second half? You know, is it just that, or is it, you know, you still have to design the protocol? I'm just trying to understand what all because we've been talking about this program for almost a year and a half now.

Shankar Musunuri -- Co-Founder, Chairman, and Chief Executive Officer

Yeah, no, so, RK, the facility construction will be completed -- it's a GMP facility -- by the end of this year. It's on target. And then, as you know, as a GMP facility, it takes a few months for getting the qualification done, and then they'll be ready to produce NeoCart in that facility. In the interim, obviously, the team is going to prepare, you know, CMC and clinical sections.

And they'll continue to, you know, update those so that they're ready for submission next year before they start the clinical trial. And we do have RMAT designation, right, I just wanted to remind, Regenerative Medicine Advanced Therapy designation with FDA. So, when we have any questions in the interim, we can always reach out to them and get clarifications.

Unknown speaker

Perfect. Thanks. Thanks for taking the questions.

Operator

Our next question comes from the line of Robert LeBoyer from Noble Capital Markets. Please go ahead.

Robert LeBoyer -- Noble Capital Markets -- Analyst

Good morning, everyone. My question has to do with the upcoming data presentation. And I was wondering if you could disclose whether the data to be presented will update the previous data on all patients or whether it will just be in new patients that haven't been reported. And wondering if there are any endpoints that you could share with us at this point.

Arun Upadhyay -- Chief Scientific Officer

Thank you. So, yeah, definitely, we'll be providing detailed, you know, update when we kind, of you know, present this data to the market. But to address your first part of the question whether it is going to include the percent we presented in our previous, you know disclosure, yes. So, we will include those subjects as well as some new subjects which has, you know, completed additional follow-up visits.

So, it will be a combination of both, yeah.

Robert LeBoyer -- Noble Capital Markets -- Analyst

OK. Great. And you had mentioned corporate partnerships for the gene therapy. Can you discuss any type of arrangements whether they're going to be research and development, or just marketing, or any objectives to the business development activities?

Shankar Musunuri -- Co-Founder, Chairman, and Chief Executive Officer

I mean, Robert, this is a -- yeah, it's a loaded question. Obviously, we'll be open to -- when you seek partnerships at this stage as a biotech, you know, big pharma established with a infrastructure and everything else, obviously, your first target is going to be commercial. This has, as you know, complex sciences involved in these clinical trials. Obviously, when you're going into phase 3, once we have the data out, we'll obviously work very hard with any potential partners. And obviously, as you know, if they are interested in commercial development, they would be interested in phase 3 program too.

So, I think we'll keep our options open whatever can maximize our value for Ocugen as well as, you know, make sure we have ability to provide market access to patients who desperately need this product.

Robert LeBoyer -- Noble Capital Markets -- Analyst

OK, great. Thank you very much.

Operator

Our final question comes from the line of Daniil Gataulin from Chardan. Please go ahead.

Daniil Gataulin -- Chardan Capital Markets -- Analyst

Hey, good morning, guys. Thank you for taking that question. Got a couple. One for -- for OCU410 program.

Just wanted to ask, strategically, how do you see it positioned, long-term view, as a stand-alone approach or in combination with anything else and the patient population that you'll be targeting? And the second question is for the inhaled vaccine series. You know, can you comment on your interactions to date regarding the funding and, particularly, if you can comment on the interest in this program given that there are several other approved options? Thank you.

Arun Upadhyay -- Chief Scientific Officer

Thank you. So, I'll take the first question. So, yeah, we are thinking of, you know, taking it just stand-alone product. And to begin with, definitely, we'll be targeting the advanced form of AMD, that is geographic atrophy.

But subsequently, you know, based on the outcome, it -- it may be further developed for, you know, early and intermediate stage depending on the clinical benefit coming from the GA trial. And regarding, like, how we -- how we see this product, you know, against other what we see in the market, so as you all know that AMD is a multifactorial disease. And so, far we have been, you know, seeing in this space that most of the companies are focused on targeting only one pathway. And by, like, very nature of this disease, you know, being multifactorial in nature, there are various causes which lead to this disease. So, our product has potential to target all those -- those pathways which are linked to the AMD pathogenesis. So, we believe that this could be a differentiated product and -- and has potential to -- to offer a better, you know, clinical benefit compared to what -- what we have right now.

Shankar Musunuri -- Co-Founder, Chairman, and Chief Executive Officer

And, Daniil, good morning. The second question related to government funding, again, we have been working with, you know, various agencies and will provide an update. Obviously, the current products, as you know, the vaccines we have, especially for COVID, they lack a couple of things. One, they're struggling to control the transmission. There's a lot of data available so -- right now.

And also, they lack durability. So, what would be ideal option going into the future? I think there is definitely a need for mucosal vaccines. The scientific community agrees on that. And also, that will provide systemic as well as mucosal immunity.

So, you can actually potentially prevent at the target of viral entry into mucosal system. The second thing is durability. I mean, we believe, and the scientific community believes, that COVID vaccinations, in order to gain the compliance with public, you cannot keep on vaccinating every three months. That's why, you know, people get vaccine fatigue. So, you need to move into like annual vaccinations such as flu so compliance rate will go up.

So, that's the intent. I think as, you know, we stated, there are ex-U.S. trials with inhalation vaccine using a similar technology showed durability up to one year. That's really important.

So, there are two things: controlling transmission and durability are very important for next-gen vaccines. And we believe our platform-technology inhalation vaccines for COVID and flu, they can provide that.

Daniil Gataulin -- Chardan Capital Markets -- Analyst

Thank you so much.

Operator

This concludes the Q&A portion. I will now turn the call back over to chairman and CEO, Dr. Shankar Musunuri.

Shankar Musunuri -- Co-Founder, Chairman, and Chief Executive Officer

Thank you, operator. In closing, I'd like to recognize the entire team for their resilient efforts to advance our patient-centric mission. To our shareholders and partners, thank you for your ongoing trust and support. We're already well into the second half of 2023 and are steadfast in our commitment to transparency, informed decision-making based on sound scientific principles, and a tireless work ethic dedicated to excellence in all phases of research, development, and clinical testing. We remain confident that we will be able to fulfill our mission of developing novel therapies with innovative discovery to bring to market effective treatments for patients suffering from a range of conditions that currently lack treatment options.

We look forward to sharing more details on our progress in the coming months.

Tiffany Hamilton -- Head of Corporate Communications

Thanks, everyone. Have a great day.

Duration: 0 minutes

Call participants:

Tiffany Hamilton -- Head of Corporate Communications

Shankar Musunuri -- Co-Founder, Chairman, and Chief Executive Officer

Jen Kim -- Cantor Fitzgerald -- Analyst

Arun Upadhyay -- Chief Scientific Officer

Unknown speaker

Robert LeBoyer -- Noble Capital Markets -- Analyst

Daniil Gataulin -- Chardan Capital Markets -- Analyst

More OCGN analysis

All earnings call transcripts

GOTHENBURG, SE / ACCESSWIRE / August 21, 2023 / IRLAB Therapeutics (STO:IRLAB-A)(FRA:6IRA), a company discovering and developing novel treatments for Parkinson's disease, today announced further information on the results from mesdopetam's clinical Phase IIb study in Parkinson's disease levodopa-induced dyskinesias (PD-LIDs) and Phase III preparatory Phase I studies performed by Ipsen. The results indicate that mesdopetam has dose-dependent anti-dyskinetic and anti-parkinsonian effects combined with a tolerability and safety profile not different from placebo, giving mesdopetam a unique position in the competitive landscape. The comprehensive data package developed by IRLAB and Ipsen provides a solid foundation for continuing the development of mesdopetam to Phase III. IRLAB now proceeds with preparations for Phase III, requesting an end-of-Phase 2 meeting with the US FDA to define the Phase III study program. A webcast in conjunction with the announcement will be held on August 22, 2023, at 10:00 CEST, access details below.

In-depth analyses of the Phase IIb study data have now been completed confirming the great potential of mesdopetam in people living with PD-LIDs. The objectives of the study were to investigate the efficacy and safety of three doses of mesdopetam (2.5, 5, and 7.5 mg twice daily (b.i.d.)), as compared to placebo, in people living with PD-LIDs to guide dose selection for Phase III. Top-line results from the Phase IIb study of mesdopetam in PD-LIDs were communicated in January 2023.

In the in-depth evaluation of the Phase IIb study data the effects in subjects based both on their randomized dose (FAS population) and based on the real dose received in subjects compliant with the protocol (PS population), were analyzed. Since dose adjustment was allowed at one time during this study, analyses based on the real dose received are important to get a full understanding of the dose dependency and the treatment effects. These analyses provide the basis for dose selection for Phase III.

Story continues

"We are pleased to see clear and clinically meaningful anti-dyskinetic effects of mesdopetam in the analyses of the Phase IIb study data. Consistent dose-response patterns were observed across the key efficacy endpoints assessing dyskinesia: "good ON"-time, UDysRS, UDysRS subscales. This, in combination with unchanged MDS-UPDRS part 2 scores demonstrates that mesdopetam shows anti-dyskinetic efficacy without affecting normal motor function, or negatively impacting the anti-parkinsonian treatment effect of levodopa. The reduced OFF-time even indicates that mesdopetam has anti-parkinsonian effects," said Nicholas Waters, EVP and Head of R&D, IRLAB. "The dose-dependent effects make it possible to select dose for Phase III."

In the FAS, mesdopetam's treatment effect on Hauser diary "good ON" (ON-time without troublesome dyskinesia) did not reach statistical significance, whereas in the PS, among subjects taking the 7.5 mg b.i.d. dose of mesdopetam, a significant and clinically meaningful increase in "good ON"-time of 1.75 hours vs placebo ("good ON" scaled to 16 hours awake time, p=0.050) was observed.

In the FAS, mesdopetam demonstrated significant anti-dyskinetic effects, as measured by UDysRS, sum of parts 1, 3 and 4, with a reduction of 6.2 points vs. placebo (p=0.026) at 7.5 mg b.i.d. In the PS, the effect on UDysRS was dose-dependent and a reduction of 9.2 points vs. placebo (p=0.011) was observed at 7.5 mg b.i.d. In the UDysRS disability score parts 1b+4, assessing the degree of disability caused by dyskinesia, there was a reduction of 3.5 points vs. placebo (p=0.062) in the FAS and, in the PS a reduction of 5.5 points vs placebo (p=0.019) was observed at 7.5 mg b.i.d.

This means that both patients and physicians report reduced disability related to dyskinesia during mesdopetam treatment. Further, the daily time spent in OFF showed a clear dose-dependent pattern and a decrease compared to placebo in both FAS and PS, reaching 1.27 hours (p=0.051) at the 7.5 mg b.i.d. dose in the PS, indicating a direct anti-parkinsonian effect of mesdopetam in subjects treated with levodopa. Mesdopetam was well tolerated with an adverse event and safety profile on par with placebo.

"I think the mesdopetam data package is one of the most compelling available in the symptomatic treatment of Parkinson's. Mesdopetam has the rare ability to both Boost dyskinesias and Boost parkinsonism and, at the same time, appears to be well tolerated. I expect it will have both clinical utility and commercial success," said Karl Kieburtz, MD, MPH, Professor in Neurology, Former chairman of the Peripheral and Central Nervous System US FDA Advisory Committee; chairman of the Scientific Evaluation Committee for the Cooperative Studies Program, Veterans Administration, and the National Institute of Neurologic Disorders and Stroke.

The Phase I clinical studies performed by Ipsen to prepare for Phase III were successfully completed and showed favorable results. One study evaluated pharmacokinetics (PK) in different populations showing that mesdopetam has similar PK in Asian and Non-Asian populations. A second study evaluated the potential for PK drug-drug interactions, via key drug metabolizing enzymes, showing a low risk of drug-drug interaction, suggesting that neither additional clinical drug-drug interaction studies nor restrictions on future patient enrolment would be required in future clinical studies. A third study investigated elimination routes of mesdopetam showing no risk of accumulation of mesdopetam in the body. Safety data from all three studies did not reveal any new safety signals and thus, was consistent with the current knowledge of the safety profile for mesdopetam.

"The data from the three Phase I studies performed by Ipsen are encouraging as they suggest that treatment with mesdopetam will be predictable and with little variability. This will most likely result in a simple uniform dosing of the drug, which minimizes the risk of dosing errors and is an advantage for mesdopetam over current alternatives in the management of dyskinesia in Parkinson's," commented Joakim Tedroff, CMO, IRLAB.

"I am excited by the potential of mesdopetam both commercially and how it truly can benefit people living with Parkinson's. The aging global population driving the increased prevalence of Parkinson's and people developing levodopa-induced dyskinesia suggest that mesdopetam could play an important role in this large and growing unmet clinical need. I will focus on leading the continued work toward Phase III together with our regulatory, clinical, and financial advisors. We anticipate that this strengthening of our portfolio with the Phase III ready mesdopetam project will Boost our partnering and financing opportunities to advance our world-leading portfolio of treatments in Parkinson's disease," said Gunnar Olsson, CEO, IRLAB.

In conclusion, available data indicate that mesdopetam has dose-dependent anti-dyskinetic and anti-parkinsonian effects combined with a tolerability and safety profile not different from placebo, giving mesdopetam a unique position in the competitive landscape. The comprehensive data package developed by IRLAB and Ipsen provides a solid foundation for continuing the development of mesdopetam to Phase III. IRLAB now proceeds with preparations for Phase III, requesting an end-of-Phase 2 meeting with the US FDA to define the Phase III study program.

Comprehensive results of the Phase IIb study of mesdopetam in PD-LIDs will be presented at MDS Congress in Copenhagen held on August 27-31, 2023. As IRLAB secured full ownership of the mesdopetam project, all further communications concerning mesdopetam are now the responsibility of IRLAB.

For more information

Gunnar Olsson, CEO
Phone: +46 70 576 14 02
E-mail: gunnar.olsson@irlab.se

Nicholas Waters, EVP and Head of R&D
Phone: +46 730 75 77 01
E-mail: nicholas.waters@irlab.se

Webcast for investors, analysts, and media

The company will hold a live webcast in conjunction with the announcement on August 22, 2023 at 10:00 CEST.

Access webcast via link: https://channel.royalcast.com/landingpage/hegnarmedia/20230822_4/

About the Full Analysis Set (FAS)
The Full Analysis Set (FAS) consisted of all randomized and treated patients who received one or more doses and who provided post-baseline data independent of the real dose taken during the study, also commonly referred to as the ITT analysis.

About the protocol-compliant adjusted dose set (PS)
The protocol-compliant adjusted dose set (PS) consisted of patients who were compliant to the study protocol including documented compliance to the dosing regimen in the study, with adjustment to the real dose received.

About Hauser diary (patient-completed 24-hour diaries)
Clinical diaries are a standardized method for patients to assess their health status. Patients log their motor status every 30 minutes for 24 hours. Patients record whether their motor status is:

• "OFF" denotes stiffness, marked decrease of mobility or immobility

• "ON" denotes good or practically normal mobility

• "ON with troublesome dyskinesias" is when the patient is troubled by involuntary twisting and turning movements.

• Additionally, sleep time is recorded

About Unified Parkinson's Disease Rating Scale (MDS-UPDRS)
The Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a standardized and validated estimation scale developed for assessment of symptoms in Parkinson's disease. The instrument has been tested for good reliability and validity and consists of the following four parts:

• Part I - Non-Motor Aspects of Experiences of Daily Living

• Part II - Motor Aspects of Experiences of Daily Living

• Part III - Motor Examination

• Part IV - Motor complications of therapy

Each section has questions that rate the symptoms from 0 to 4 where higher values indicate more severe symptoms.

About Unified Dyskinesia Rating Scale (UDysRS)
The Unified Dyskinesia Ratings Scale (UDysRS) evaluates the involuntary movements that can be associated with long-term treatment with dopaminergic medication. The UDysRS has four parts:

• Part 1 - Historical Disability (patient perceptions) of ON-Dyskinesia impact

• Part 2 - Historical Disability (patient perceptions) of OFF-Dystonia impact

• Part 3 - Objective Impairment (dyskinesia severity, anatomical distribution over seven body regions, and type (choreic or dystonic) based on four activities observed or video-recorded

• Part 4 - Objective disability based on Part III activities

The modified UDysRS was used in the Phase IIb trial, which constitutes Part 1, Part 3 and Part 4.

About Phase IIb study with mesdopetam

The Phase IIb study with mesdopetam in people with Parkinson's disease affected by levodopa-induced dyskinesias (LIDs) was a randomized, double-blind, placebo-controlled study with the purpose of evaluating anti-dyskinetic effect and safety/tolerability of three different doses of mesdopetam and to define the right dose of the drug for Phase III studies. Subjects on stable regimen of anti-parkinson medication, experiencing troublesome dyskinesia, were randomized to placebo, mesdopetam 2.5, 5, or 7.5 mg b.i.d. for 12 weeks. According to the study protocol patients were allowed to adjust their dose one time during the study. The primary endpoint was daily ON-time without troublesome dyskinesia ("good ON") measured by Hauser-diaries. Secondary endpoints included UDysRS (parts 1+3+4), UDysRS disability score (1b+4), UDysRS objective score (3+4), time in different motor-states (bad ON, OFF), MDS-UPDRS, CGI, MMSE, along with pharmacokinetics, safety and tolerability. Data presented here was analyzed based on least squares mean (LS mean) differences vs. placebo using a mixed models for repeated measures (MMRM) for the Full Analysis Set (FAS) based on randomized dose, and in the protocol compliant subjects with adjustment to real dose received (PS). To adjust for variability in sleep time, Hauser diary data were also scaled to 16 hours of awake time in the PS (post-hoc).

The study randomized 156 patients distributed across four groups, three dose levels of mesdopetam and a placebo group with approximately 40 patients in each group with a treatment period of three months. The study was conducted at 46 study sites in Europe, Israel and in the US. More information can be found on clinicaltrials.gov: NCT04435431, and EudraCT number: 2020-002010-41.

About mesdopetam

The investigational drug mesdopetam (IRL790) is a dopamine D3 receptor antagonist in development as a treatment of Parkinson's disease levodopa-induced dyskinesias (PD-LIDs). The primary objective of mesdopetam is to Boost the quality of life of individuals with Parkinson's disease by reducing PD-LIDs, a debilitating condition characterized by involuntary movements that frequently develop in patients who receive prolonged/chronic levodopa treatment. The results of the completed clinical studies in the Phase I and Phase II programs demonstrate a consistent anti-dyskinetic effect of mesdopetam, good safety, and it is assessed as offering significantly better tolerability compared to existing treatments.
Preclinical studies show that mesdopetam is a potent and efficacious anti-dyskinetic drug, and that mesdopetam also has the potential to prevent the development of dyskinesia as well as treating Parkinson's disease Psychosis (PD-P).

About IRLAB

IRLAB is discovering and developing a portfolio of transformative therapies targeting all stages of Parkinson's disease. The company has its origin in Nobel Laureate Prof. Arvid Carlsson's research group and the discovery of a connection between the brain's neurotransmitters and CNS disorders. Mesdopetam (IRL790), in development for the treatment of levodopa-induced dyskinesias, has completed Phase IIb and is in preparation toward Phase III. Pirepemat (IRL752), is currently in Phase IIb, being evaluated for its effect on balance and fall frequency in Parkinson's disease. In addition, the company is also progressing the three preclinical programs IRL942, IRL757, and IRL1117 towards Phase I studies. The pipeline is driven by IRLAB's proprietary systems biology-based Integrative Screening Process (ISP) research platform. Headquartered in Sweden, IRLAB is listed on Nasdaq Stockholm (IRLAB A). For more information, please visit www.irlab.se.

Attachments

IRLAB provides update on mesdopetam's Phase IIb study data and the plans toward Phase III

SOURCE: IRLAB Therapeutics

View source version on accesswire.com:
https://www.accesswire.com/775597/IRLAB-Provides-Update-on-Mesdopetams-Phase-IIb-Study-Data-and-the-Plans-Toward-Phase-III

Arterial stiffness is a novel cause of premature heart damage among adolescents, according to a new follow-up study. The study was conducted in collaboration between Texas Children's Hospital and Baylor College of Medicine in the US, the University of Bristol in the UK, the University of Exeter in the UK, and the University of Eastern Finland, and the results were published in Atherosclerosis.

Left ventricular hypertrophy and left diastolic dysfunction are measures of structural and functional heart damage, which have been associated with an increased risk of cardiovascular-related death in adults. These cardiac measures are also used in the pediatric population as indicators of premature heart damage.

Arterial stiffness estimated from carotid-femoral pulse wave velocity has been discovered as a novel cause of increased blood pressure, insulin resistance, and metabolic syndrome in adolescents and young adults. It was also recently shown that increased blood pressure in adolescence may cause premature heart damage, but it is not known whether arterial stiffness could independently cause structural and functional damage to the heart.

The current study was conducted among 1,856 adolescents of whom 1,011 were female. The adolescents were 17 years old at baseline, and they were followed up for 7 years until young adulthood at age 24 years. Arterial stiffness, carotid intima-media thickness, and evidence of heart damage were assessed at baseline and follow-up. Signs of heart structure damage are left ventricular hypertrophy and high relative wall thickness, whereas signs of heart function damage are left ventricular diastolic dysfunction and increased left ventricular filling pressure.

During the 7-year follow-up period, the prevalence of heart structural damage among adolescents doubled. With extensive control for fat mass, muscle mass, glucose, insulin, blood pressure, lipids, smoking status, sedentary time, physical activity, socio-economic status, and family history of cardiovascular disease, and using adults' cut points for diagnosing heart damage, it was observed that adolescents in the highest tertile category of arterial stiffness and carotid intima-media thickness had a 23 - 27% increased risk of progressively worsening structural heart damage.

Only arterial stiffness appears to independently cause both structural and functional heart damage, whereas increased carotid wall thickness does not seem to have a causal role. Increased carotid wall thickness is an early indicator of atherosclerosis, whereas increased arterial stiffness describes arteriosclerosis. The study further reported that arterial stiffness caused heart damage by increasing blood pressure and insulin resistance. The increase in blood pressure explained 34% of the heart damage caused by arterial stiffness. Moreover, insulin resistance explained 15% of the heart damage caused by arterial stiffness.

We are seeing for the first time that arterial stiffness is a novel cause of several diseases such as hypertension, insulin resistance, metabolic syndrome, and heart damage in the young population. Among adults, arterial stiffness is currently being established as a cause of type 2 diabetes. We discovered that approximately 50% of the deleterious role of arterial stiffness in causing heart damage is enhanced by the mechanism of increased blood pressure and insulin resistance. Thus, preventing and lowering blood pressure and insulin resistance may potentially diminish the negative impact of arterial stiffness on the heart, by up to half."

Andrew Agbaje, physician and clinical epidemiologist, University of Eastern Finland

"Experimental and clinical intervention studies are urgently needed on comprehensive approaches to treating and reversing arterial stiffness from adolescence. At least, targeting blood pressure and insulin resistance leaves the problem half-solved," Agbaje continues.

Dr Agbaje's research group (urFIT-child) is supported by research grants from Jenny and Antti Wihuri Foundation, the Finnish Cultural Foundation Central Fund, the Finnish Cultural Foundation North Savo Regional Fund, the Orion Research Foundation, the Aarne Koskelo Foundation, the Antti and Tyyne Soininen Foundation, the Paulo Foundation, the Yrjö Jahnsson Foundation, the Paavo Nurmi Foundation, the Finnish Foundation for Cardiovascular Research, Ida Montin Foundation, Eino Räsänen Fund, Matti and Vappu Maukonen Fund, and the Foundation for Pediatric Research.