Author Archives: Aaron Csicseri

The Not-So-Far-Out Therapeutic Promise of Psychedelics

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Introduction

Throughout history, humans have had a complex relationship with psychedelics. For millennia, ancient indigenous cultures used them for spiritual and healing purposes. For example, in 2007, archaeologists in Spain discovered mushrooms found at a burial site dated more than 7,000 years old. The mushrooms found at the site were identified as psilocybin, a type of psychedelic mushroom. Fast forward to today, and psychedelics are still in use for spiritual and mystical experiences but are also being studied more scientifically as pharmacotherapy for various psychological conditions, including anxiety, depression, posttraumatic stress disorder (PTSD), dependency/addiction, eating disorders, and end-of-life distress. In fact, the FDA even released a draft guidance to industry for designing clinical trials for psychedelic drugs.

What Are Psychedelics?

Psychedelics are derived from plants and fungi, but some are also synthesized in the lab. Psilocybin, the most studied psychedelic comes from fungi, which are unique organisms that are neither plants nor animals. They share some characteristics with plants, such as the ability to photosynthesize, but they also share some characteristics with animals, such as the ability to digest food. Computational phylogenetics have revealed that fungi split from animals about 1.538 billion years ago, whereas plants split from animals about 1.547 billion years ago. This means fungi split from animals 9 million years after plants did, meaning that fungi are actually more closely related to animals/humans than to plants.

LSD, lysergic acid diethylamide; MDMA, 3,4-Methylenedioxymethamphetamine.

Psychedelics modulate brain activity and have been associated with therapeutic effects such as increased neuroplasticity and modulation of reward pathways, not dissimilar to the mechanism of action underlying conventional antidepressants. Psychedelics work by binding to the serotonin 2A receptor on neurons throughout the brain, which causes

  • The neurons to fire more rapidly
  • More effective neuronal communication between different brain regions
  • Disrupted sensory processing leading to changes in sight, hearing, taste, smell, and touch

These changes in the brain lead to alterations in perception, thought, and mood that are characteristic of a psychedelic experience.

The discovery and synthesis of lysergic acid diethylamide (LSD) in 1938 by Albert Hofmann brought about a surge of research into the use of psychedelics in the 1950s and 60s. But this research was largely halted in the 1970s due to unsubstantiated concerns about their safety and potential for abuse. However, in recent years there has been a resurgence of interest concerning the therapeutic potential of psychedelics.

How Are Psychedelics Being Studied?

As of today, psychedelics remain a Schedule 1 drug in the United States, meaning that per the federal government, psychedelics have no medical value and hold high potential for abuse. Despite this designation, the study of psychedelics is acceptable under highly regulated and controlled circumstances. Anyone conducting research with these drugs must obtain approval from the US Food and Drug Administration (FDA) and request a Schedule 1 license from the Drug Enforcement Administration (DEA).

Several recent studies have shown the effectiveness of psychedelics:

LSD, lysergic acid diethylamide; MDMA, 3,4-Methylenedioxymethamphetamine; PTSD, posttraumatic stress disorder.

It is important to note that this field is still in its infancy, with the benefits of psychedelics yet to be proven in large, randomized trials. With psilocybin, the best-characterized psychedelic, several early-phase studies have been conducted. The most notable of these is a phase 1 study (NCT04052568) designed and conducted by Johns Hopkins University, investigating psilocybin in patients with anorexia nervosa.6

There are several explanations for why this field is moving so slowly, including but not limited to

  • Legal and regulatory hurdles
  • Difficulty blinding psychedelic trials because of the obvious effects of the drug
  • Patient expectations of efficacy are often too high and not realistic
  • Social perceptions as well as economic issues make enrollment challenging
  • Requirement of special authorization to study a Schedule 1 drug
  • Limited funding at academic institutions for the large trials needed to produce robust data

FDA Draft Guidance Concerning Psychedelic Clinical Trials

In an effort to highlight fundamental considerations for researchers investigating the therapeutic use of psychedelic drugs, the FDA recently released their first FDA draft guidance to industry for designing clinical trials for psychedelics.7 Key takeaways from the guidance include

  • The FDA recognizes that psychedelic drugs have therapeutic potential for the treatment of a range of medical conditions
  • The FDA is willing to work with sponsors to develop psychedelic drugs for clinical use
  • The FDA has identified challenges that need to be addressed in the development of psychedelic drugs and provides sponsors with recommendations for addressing these challenges

It is important to point out the contradiction between the federal status of psychedelics as Schedule 1 drugs and the simultaneous FDA acknowledgement that these agents do in fact hold therapeutic potential. This will need to be remedied through federal law, opening the door to more pharmaceutical industry investment in clinical trials.

Conclusions

Our current knowledge of psychedelics owes much to our ancient ancestors’ wisdom in exploring these substances. Today, despite being classified as Schedule 1 drugs at the federal level, psychedelics are being studied more seriously for their potential to treat psychological conditions. The recent release of the FDA’s draft guidance for designing clinical trials on psychedelics demonstrates a growing recognition of their therapeutic potential. As we move forward, rigorous research will be essential to fully understand the advantages and risks of psychedelics, potentially leading to groundbreaking medical treatments in the future.

References

  1. Griffiths RR, et al. Psilocybin produces substantial and sustained decreases in depression and anxiety in patients with life-threatening cancer: A randomized double-blind trial.
    J Psychopharmacol. 2016;30(12):1181-1197.
  2. Noller GE, et al. Ibogaine treatment outcomes for opioid dependence from a twelve-month follow-up observational study. Am J Drug Alcohol Abuse. 2018;44(1):37-46.
  3. Davis AK, et al. Effects of psilocybin-assisted therapy on major depressive disorder: a randomized clinical trial. JAMA Psychiatry. 2021;78(5):481-489.
  4. Mitchell JM, et al. MDMA-assisted therapy for severe PTSD: a randomized, double-blind, placebo-controlled phase 3 study. Nat Med. 2021;27(6):1025-1033.
  5. Holze F, et al. Lysergic acid diethylamide-assisted therapy in patients with anxiety with and without a life-threatening illness: a randomized, double-blind, placebo-controlled phase II study.
    Biol Psychiatry. 2023;93(3):215-223.
  6. Effects of psilocybin in anorexia nervosa. ClinTrials.gov identifier: NCT04052568. Updated: May 6, 2023. Accessed: July 21, 2023. https://clinicaltrials.gov/study/NCT04052568?term=NCT04052568&rank=1.
  7. US Food and Drug Administration, Center for Drug Evaluation and Research. Psychedelic Drugs: Considerations for Clinical Investigations. Guidance for Industry. US Food and Drug Administration; June 2023. Accessed July 28, 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/psychedelic-drugs-considerations-clinical-investigations.

Aaron Csicseri, PharmD
Aaron Csicseri, PharmD
Senior Scientific Director

Dr. Csicseri joined the ProEd team in November 2017 as a scientific director, responsible for scientific leadership, content development, strategic input, and effective moderation of team meetings. Aaron has extensive experience guiding Sponsor teams through the AdCom preparation process. He received his PharmD at the University of Buffalo, where he studied the clinical curriculum. Aaron has 10+ years of experience as a medical director/clinical strategist in the accredited medical education field (CME), as well as in the non-accredited PromoEd sphere. Over the past 6 years, he has been supporting Sponsors in their preparations for FDA and EMA regulatory meetings in a wide variety of therapeutic areas. Aaron is based in Grand Island, NY, just outside Buffalo.

Connect with Aaron on LinkedIn.

 

Communicating the Complexities of Subgroup Analyses at an AdCom

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Within clinical trials, exploratory or post-hoc subgroup analyses are widely recognized as only “hypothesis generating” due to their high potential for bias and/or misleading interpretation. This is the main reason why Sponsors cannot make efficacy claims or seek regulatory approval based on evidence of efficacy in a certain subgroup unless that benefit is consistent with the broader trial population and unless the trial is positive overall for the intention-to-treat (ITT) population. This begs the question, “Is it acceptable to use an exploratory subgroup analysis to restrict an indicated population when the data suggest less benefit in a particular subgroup?”

That is exactly what FDA asked the Oncologic Drugs Advisory Committee (ODAC) to consider in the case of the PROpel data, based on their conclusion that the combination of olaparib plus abiraterone has a favorable benefit/risk only in the subgroup of patients with advanced prostate cancer who test positive for a mutation in the BReast CAncer (BRCA) gene, which regulates homologous recombination repair of DNA. However, one might argue that the exploratory/post-hoc analysis on which FDA based their conclusion remains, by its very nature, fraught with potential for bias and/or misleading interpretation and is thus only hypothesis generating.

“Is it acceptable to use an exploratory subgroup analysis to restrict an indicated population when the data suggest less benefit in a particular subgroup?”

In the era of precision medicine, we expect that treatment choices are driven by biomarkers that can predict clinical benefit. In the case of poly ADP-ribose polymerase (PARP) inhibitors, like olaparib, BRCA mutations or deficiencies in homologous recombination repair (HRR) can predict clinical benefit. But there may be clinical situations where biomarker testing is limited or where patients without BRCA mutations might benefit from treatment with a PARP inhibitor. Indeed, the science suggests that patients with metastatic castration-resistant prostate cancer (mCRPC) may benefit from the combination of a PARP inhibitor with an antiandrogen, like abiraterone, regardless of BRCA mutation status, based on the synergistic activity of these 2 drug classes. In addition, the majority of patients with mCRPC (especially in disadvantaged communities) do not have definitive biomarker testing for BRCA mutations, usually due to cost and/or lack of available tumor tissue. That is the context for the PROpel study investigating the combination of the PARP inhibitor olaparib (Lynparza) plus abiraterone (Zytiga) as first-line treatment of mCRPC.

The PROpel trial was designed to assess the activity of this combination in the broad, unselected, ITT population, and data on BRCA mutation status by ctDNA and tissue tests were collected for the purpose of exploratory subgroup analysis. The trial met its primary endpoint in the ITT population, demonstrating a statistically significant 40% improvement in radiologic progression-free survival (rPFS). Therefore, AstraZeneca was seeking a broad indication that includes BRCA mutant, BRCA wild-type, and BRCA unknown patients. The Sponsor also presented evidence that patients without BRCA mutations or with unknown BRCA status benefited from the combination of olaparib plus abiraterone. However, on April 28, 2023, the ODAC voted 11 to 1 (with 1 abstention) to limit use of the combination to men whose tumors tested positive for a BRCA mutation, which represents only about 10% of patients with mCRPC. This was based on post-hoc subgroup analyses that created the perception of a less favorable benefit/risk in the BRCA wildtype or unknown patients.

On April 28, 2023, the ODAC voted 11 to 1 (with 1 abstention) to limit use of the combination to men whose tumors tested positive for a BRCA mutation

Dr. Chana Weinstock articulated the FDA’s position on this issue at the April 28 ODAC meeting. She said that the Agency discourages using subgroup analysis to try to argue for efficacy in a specific group, particularly in a failed trial (although PROpel was a positive study). However, she highlighted historical precedent for limiting indications based on post-hoc subgroup analysis suggesting that certain subgroups might have compromised safety or a potential overall survival detriment. Finally, she cited the FDA guidance that states that if a trial only shows benefit in a selected subgroup, the indication may be limited to a narrower population, especially if that same signal is observed in other comparable trials. (Figure 1)

Figure 1

 

Jorge Nieva, section head of solid tumors at the University of Southern California, objected to restricting the indication to only those patients with known BRCA mutations, saying “I worry that the approach used in this application can justify removing any subgroup from any application where that subgroup has an OS curve that crosses one. FDA seems to be looking at these OS curves in a vacuum and is ignoring the corroborating evidence that some non-BRCA patients could benefit significantly.”

“I worry that the approach used in this application can justify removing any subgroup from any application where that subgroup has an OS curve that crosses one.”

It is common for Sponsors to find themselves in this situation at ODAC where the data are somewhat ambiguous and the arguments/counter arguments are highly statistical in nature. This is especially true for subgroup analyses. The key communication goal when addressing an advisory committee is to make your position as easy to understand as possible by breaking down your argument into digestible bites. If your messages are too complex, statistical or philosophical, the committee may not fully appreciate your position. When this occurs at ODAC, the committee typically defers to the FDA’s position.

Aaron Csicseri, PharmD
Aaron Csicseri, PharmD
Senior Scientific Director

Dr. Csicseri joined the ProEd team in November 2017 as a scientific director, responsible for scientific leadership, content development, strategic input, and effective moderation of team meetings. Aaron received his PharmD at the University of Buffalo, where he studied the clinical curriculum. He has 10+ years of experience as a medical director/clinical strategist in the accredited medical education field (CME), as well as in the non-accredited PromoEd sphere. Over the past 5 years, he has been guiding Sponsor teams in their preparations for FDA and EMA regulatory meetings in a wide variety of therapeutic areas. Aaron is based in Grand Island, NY, just outside of Buffalo, New York.

Connect with Aaron on LinkedIn.

 

The Rise of ChatGPT in the Pharma Industry

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Artificial Intelligence (AI) is rapidly transforming how we work, and the pharmaceutical industry is no exception. In particular, the emergence of Generative Pre-trained Transformer (GPT) models has the potential to revolutionize several aspects of the biotech business, including drug discovery and clinical trials. According to a recent survey, 39% of healthcare professionals see AI (including ChatGPT), as the most disruptive emerging technology in 2023. Let’s explore a few ways this incredible new tech might create efficiencies for industry.

AI/ChatGPT Models in Drug Discovery

  • Analyze vast amounts of data (research papers, clinical trials, etc.)
  • Identify previously unknown patterns and insights that could inform drug discovery
  • Reveal new drug targets
  • Predict (eventually) the efficacy and safety of potential drugs prior to clinical trials, lowering safety risks for future trial subjects

Drug Discovery Use Case: DSP-1181 is the first example of a drug originally identified by AI to enter clinical trials. In rapid time, Sumitomo Dainippon Pharma developed and secured approval for obsessive-compulsive disorder (OCD) in Japan in 2020. The process of designing, synthesizing, and testing the molecule took just 12 months—compared to an average of 4.5 years for traditional drug discovery methods.

AI/GPT Models in Clinical Trials

  • Accelerate patient recruitment/enrollment
  • Generate predictive “synthetic data” that can be used in place of trial subjects, lowering patient risks as well as decreasing recruitment requirements
  • Manage large amounts of trial data more efficiently, freeing up trialists to focus on non-administrative tasks
  • Increase patient trial engagement via GPT-powered chatbots that provide frequent personalized communication both in terms of giving patient advice and in receiving input on trial design from the patient…ultimately improving trial quality
  • Automate reporting of trial information, such ADR case reports, required for ongoing pharmacovigilance

Clinical Trials Use Case: The phase 3 ATTR-ACT study was a collaboration between Pfizer and Saama Technologies in which Saama provided their AI-powered clinical analytics platform to accelerate patient recruitment. This partnership resulted in significantly reduced recruitment time and cost. The trial enrolled more than 1,000 patients with transthyretin amyloid cardiomyopathy in just 6 months—compared to an average of 1 to 2 years for traditional patient recruitment efforts in this disease.

AI/ChatGPT clearly have the potential to create expansive evidence-based efficiencies for pharmaceutical and biotechnology companies. Despite the potential benefits described above, it’s important to note that AI/ChatGPT are only as effective as the data they are trained on, which, in reality, can be biased and have limitations. This is why it will always be important for treating clinicians to be involved in the training and implementation of AI/ChatGPT and to provide their interpretation of the data produced by such technologies.

Currently, AI/ChatGPT do not always provide accurate summaries of data, which can lead to misinterpretations and what are sometimes referred to as confident response errors (see AI hallucinations). However, as the technology develops, it’s likely these issues will be resolved, making this unexpected and disruptive technology an essential tool for the pharma industry to use in a variety of areas…many of which haven’t even been dreamed of yet.

Aaron Csicseri, PharmD
Aaron Csicseri, PharmD
Senior Scientific Director

Dr. Csicseri joined the ProEd team in November 2017 as a scientific director, responsible for scientific leadership, content development, strategic input, and effective moderation of team meetings. Aaron has extensive experience guiding Sponsor teams through the AdCom preparation process. He received his PharmD at the University of Buffalo, where he studied the clinical curriculum. Aaron has 10+ years of experience as a medical director/clinical strategist in the accredited medical education field (CME), as well as in the non-accredited PromoEd sphere. Over the past 5 years, he has been supporting Sponsors in their preparations for FDA and EMA regulatory meetings in a wide variety of therapeutic areas. Aaron is based in Grand Island, NY, just outside Buffalo.

Connect with Aaron on LinkedIn.

 

Multiple Data Sources Show That Decentralized Clinical Trials Pay Off

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Traditional clinical trials are typically conducted at central locations or sites that patients must travel to in order to be evaluated and treated by trial investigators. These are referred to as centralized clinical trials.

In contrast, a decentralized clinical trial (DCT) has no set location for patients to report to. Rather, clinical trial activities are moved to more local settings (eg, the patient’s own home), which increases trial accessibility, and digital tools are often used to communicate with study participants and collect data. These types of trials are also referred to as patient-centric trials or virtual trials. Other elements of DCTs include

  • Online recruitment
  • eConsent
  • Wearables
  • Telemedicine visits (eg, remote adverse event reporting)

The incorporation of decentralized elements into clinical trials has been gaining traction since it began becoming more popular during the COVID-19 pandemic, which made it all but impossible to conduct traditional centralized clinical trials.

Since then, regulatory agencies around the globe, particularly in the United States, have shown increasing support for incorporation of DCT elements into registrational clinical trials. Here we summarize 3 reports that focus on the benefits of incorporating decentralized elements into clinical trials vs a traditional clinical trial design.

DCT Approach Can Increase Return on Investment

1) Tufts Center for the Study of Drug Development (CSDD) conducted a modeling study to evaluate the financial benefits of incorporating DCT elements into clinical trials. The results indicated that

  • DCT elements have the greatest impact on reducing clinical cycle times (Phase 2 and Phase 3), and also significantly reduce screen failure rates and protocol amendments
  • Incorporating DCT elements increases return on investment (ROI) by nearly 5-fold in Phase 2 trials and 13-fold in Phase 3 trials

The Tufts CSDD modeling study ultimately found that incorporating DCT elements can increase a drug’s market value by $20 million (if applied in both Phase 2 and Phase 3). The study concluded that DCTs substantially increase financial value based on key performance indicators.

2) IQVIA compared DCTs with more traditional trials. The study reviewed >300 DCTs and selected 12 that completed recruitment. Overall, the analysis showed that sponsors can achieve faster and less expensive clinical trials using DCT-driven protocols. Moreover, the analysis showed that DCT elements contribute to increased patient engagement, meaning that they are more involved and invested in the trial and in their own healthcare decisions. With respect to speed and efficiency, there were substantial reductions in

  • Time from final protocol to first patient in (↓49%)
  • Accrual time (↓78%)
  • Screen failure rate (↓39%)
  • Protocol deviations (↓54%)

The IQVIA analysis concluded that DCTs deliver a significantly better ROI and offer measurable benefits to sponsors in terms of time and cost efficiencies at virtually every point in the clinical research journey.

DCT Approach Results in Higher Quality Clinical Trials

1) Medidata surveyed 400 clinical trial executives who reported the average number of studies including at least one decentralized element was 43% before the pandemic, the current average is 55%, and the predicted average in 5 years is 66%. All respondents said there were clear benefits to a DCT approach, including improvements in

  • Patient recruitment and retention
  • Patient experience and overall engagement/investment
  • Compliance and governance adherence
  • Data quality
  • Access to real-time data to make real-time decisions

The Medidata report also highlighted potential barriers to conducting DCTs, including

  • Cost and investment in new technologies
  • Training employees in the use of new technologies
  • Relative lack of regulatory guidance on DCTs

The Medidata survey concluded that the COVID-19 pandemic has led to permanent improvements across clinical trial processes that create a more streamlined and efficient operating model and put the patient experience front and center when designing clinical trials.

The overall message conveyed by all 3 analyses is that incorporating decentralized elements into clinical trials can pay off big for Sponsors in multiple ways.

Aaron Csicseri, PharmD, is a clinical pharmacist with 15+ years of experience in medical communications, from developing accredited and promotional medical education to helping clients prepare for FDA Advisory Committee meetings and other health authority interactions. Connect with Aaron on LinkedIn.

 

Surrogate Endpoints for Accelerated Approval

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Surrogate endpoints have been used for accelerated approval (AA) since the early 1990s, playing a vital role in getting therapies for serious conditions to patients sooner. The AA pathway was first created in 1992 to accelerate the approval of drugs intended to treat “serious conditions that fill an unmet medical need.” In the intervening 30+ years, surrogate endpoints have played a major role in oncology and rare disease clinical trials, but their appropriate use is still being debated in the literature. Most often that debate centers around whether an endpoint is a true surrogate that predicts clinical benefit in the clinical context in which it is being used.

What is a “surrogate”endpoint? How is it different from “clinical outcome”endpoint?

A “surrogate” endpoint is a biomarker, lab measurement, radiographic image, physical sign, or other measure that is “reasonably likely to predict clinical benefit” whereas a “clinical outcome” endpoint is one that “directly measures clinical benefit.” Importantly, the FDA definition of clinical benefit is how a patient feels, functions, or survives.

To illustrate the difference between surrogate and clinical endpoints, below are some oncology-specific examples:

Surrogate Endpoints Clinical Outcome Endpoint
Progression-Free Survival (PFS) Overall Survival (OS)
Objective Response Rate (ORR)
Duration of Response (DoR)

The FDA publishes a Surrogate Endpoint Table updated every 6 months and listing surrogate endpoints that can support approval of a drug or a biological product under both accelerated and traditional approval pathways.1 The FDA encourages development of “novel” surrogate endpoints; a novel endpoint can become established as a surrogate based on persuasive evidence that it predicts clinical benefit in the context of a specific disease and patient population. The FDA determines the acceptability of a surrogate endpoint on a case-by-case basis, dependent on context and influenced by the disease, patient population, therapeutic mechanism of action, and currently available treatments (ie, unmet need for new treatments). If a surrogate endpoint was previously used to support AA, but subsequent confirmatory trials consistently fail to demonstrate the expected clinical benefit, that surrogate endpoint should no longer be accepted for that use.

When is it appropriate to use a surrogate endpoint?

The main purpose for using a surrogate endpoint is to shorten clinical development timelines or improve the feasibility of clinical studies in rare diseases where the number of patients is limited and large, controlled studies are challenging. In many cases, a surrogate endpoint can be reached much sooner and with fewer patients than a clinical outcome endpoint such as overall survival (OS), which is a direct measure of clinical benefit. Sponsors must think about this in the context of the specific disease and indication for which they are developing the drug.

For example, in cancer patients with a long life expectancy, a surrogate endpoint such as progression-free survival (PFS) can provide a much earlier readout than a clinical outcome endpoint such as overall survival (OS). If PFS has been shown to correlate with OS in that specific disease and indication, there is a good chance that the confirmatory trial would be able to show an OS benefit. However, in some cases this can be challenging.

In the context of rare genetic diseases, for example, the surrogate endpoint is often a biomarker that can be easily measured with precision and that is reasonably likely to predict how patients feel or function. Because clinical measures of how patients function over time can be difficult to assess with precision, they often require much larger studies to demonstrate a clinically meaningful effect. For example, in Duchenne muscular dystrophy, rather than measuring functional outcomes such as ability to walk, which can vary from one day to the next, researchers will often use a surrogate endpoint such as quantitative measurements of dystrophin protein expression.

In severe respiratory diseases, measures of lung function are often used as a surrogate to predict how well the patient can perform activities of daily living, which can often be difficult to measure with precision. These few examples illustrate how surrogate endpoints can be used to facilitate clinical research.

How much time is saved by using these endpoints?

The amount of time saved by using a surrogate endpoint is disease dependent. For example, use of PFS rather than OS in breast cancer can save almost a full year, whereas the use of response rate (RR) versus OS can save 19 months.2 It all depends on the natural history of the disease and the nature of the endpoint being studied. So, while the use of surrogate endpoints can save time on the front end, and while patients will benefit sooner, the tradeoff is that the sponsor must invest in the development of an additional post-approval confirmatory trial—and there is no guarantee that a direct clinical benefit will be confirmed. Thus, there is a chance that the patient might be taking a drug that turns out not to help them in the long run.

What are “validated” surrogate endpoints?

A “validated” surrogate endpoint meets a higher standard and can be used to support full approval. This requires that the endpoint be “supported by a clear mechanistic rationale and clinical data providing strong evidence that an effect on the surrogate endpoint predicts a specific clinical benefit.”3

Two examples include:

  • HbA1c predicting improvements in long-term complications of type 2 diabetes mellitus
  • Virologic suppression of HIV as a proxy for preventing progression to AIDS

More than 75% of approvals that used a surrogate endpoint came through the traditional pathway using a validated surrogate endpoint.3 The AA pathway does not require the use of validated surrogate endpoints.

Aaron Csicseri, PharmD, Aaron has 10+ years’ experience as a Senior Scientific Director, Medical Director, or Clinical Strategist within the medical communication field. He is responsible for overseeing and developing high-quality scientific and medical content that incorporates key communication objectives and accurate representation of data. Aaron is experienced in the development of strategic scientific communication platforms, strategic publication planning and implementation, medical expert outreach and engagement, guiding and executing medical education programs, and support for medical affairs. He received his PharmD from the University of Buffalo.

 

Sources:

  1. US FDA. Table of Surrogate Endpoints That Were the Basis of Drug Approval or Licensure. Updated February 28, 2022. Accessed March 8, 2022. https://www.fda.gov/drugs/development-resources/table-surrogate-endpoints-were-basis-drug-approval-or-licensure
  2. Chen EY, Joshi SK, Tan A, Prasad V. Estimation of study time reduction using surrogate end points rather than overall survival in oncology clinical trials. JAMA Intern Med. 2019;179(5):642-647.
  3. US FDA. Surrogate Endpoint Resources for Drug and Biologic Development. Updated July 24, 2018. Accessed March 8, 2022. https://www.fda.gov/drugs/development-resources/surrogate-endpoint-resources-drug-and-biologic-development