Neurology Takes a Page Out of the Oncology Playbook of FDA Accelerated Approvals

The field of neurology is experiencing a significant upswing in innovative therapeutic development, propelled by advances in genetics, neuroimaging techniques, and biomarker research. However, neurological diseases are inherently difficult to treat, and there remains an urgent need to rapidly translate these advances into more effective treatments. It is timely then, that several recent drug approvals in neurology have benefited from FDA’s Accelerated Approval pathway— designed to expedite access to promising drugs to treat serious, life-threatening conditions with a high unmet medical need—a regulatory path most often traveled by oncology drugs.

Four recent accelerated approvals—2 for amyotrophic lateral sclerosis (ALS) and 2 for Alzheimer’s disease—appear to support a shifting regulatory approach to neurological drugs by the FDA, particularly in its willingness to deploy regulatory flexibility.

The ALS Treatment Landscape is Evolving Rapidly

On April 25, 2023, the FDA granted accelerated approval to Biogen’s Qalsody™ (tofersen), indicated to treat a rare, genetic form of ALS mediated by the superoxide dismutase 1 (SOD1) gene, referred to as SOD1-ALS. The approval was based on evidence that tofersen significantly reduced neurofilament light chain (NfL)—a marker of axonal degeneration that is elevated in the blood of many neurologic and neurodegenerative conditions—which correlated with a delay in disease progression and death. Although a failed clinical trial proved a barrier to full approval, the Peripheral and Central Nervous System Drugs Advisory Committee reviewed the data on March 22, 2023, and voted in favor of NfL’s utility as a surrogate endpoint supporting conditional approval. This marks the first instance of a blood biomarker being effectively used as a surrogate endpoint to secure accelerated approval for a neurology drug and underlines the potential benefits of incorporating NfL—and other blood biomarker measurements—into other neurology trial designs.

The FDA’s review of Amylyx’s Relyvrio™ (AMX0035)—a fixed-dose combination of sodium phenylbutyrate plus taurursodiol—was a closely watched regulatory event last year. Marginal efficacy in a phase 2 trial initially prompted the FDA to call for more data; however, they ultimately decided to review the application. Two advisory committee meetings were convened in 2022 and reached opposing outcomes. Panelists on the Peripheral and Central Nervous System Drugs Advisory Committee (PCNS) voted in favor of approving Relyvrio™, just months after voting against the drug. The committee appeared to be swayed by additional analyses of survival benefit, a more acute focus on the unmet need, the FDAs own emphasis on exercising regulatory flexibility, and the sponsors statement that it would withdraw their drug if their phase 3 confirmatory trial fails. Subsequently, the FDA granted accelerated approval of Relyvrio™ for the treatment of ALS in September 2022.

A review in JAMA of oncologic drugs approved between 2000 and 2016 revealed that oncology drugs granted accelerated approval demonstrated a median overall survival (OS) benefit of 2.4 months1. Comparing this to Amylyx’s Relyvrio, which exhibited a 4.8-month median OS benefit, illustrates both a heightened standard of approval for neurologic drugs by the FDA and its advisory committees, and a gulf in the perception of meaningful clinical benefit in the fields of oncology and neurology. Patients and caregivers will argue that the benefit of adding a few months—and vital quality-of-life improvements—to the 3- to-5-year life expectancy of a patient suffering from ALS is just as clinically meaningful as in advanced cancers.

Alzheimer’s Accelerated Approvals Signal Renewed Scrutiny of Surrogate Endpoints

One key factor contributing to the success of accelerated approvals in oncology is the use of surrogate endpoints. These are indirect measures of clinical benefit that can be assessed more quickly than traditional endpoints, like overall survival, and are reasonably likely to predict clinical benefit.

The utility of amyloid-beta protein buildup in the brain (assessed by brain imaging) as a surrogate endpoint believed to be correlated with cognitive decline in Alzheimer’s disease was greenlighted with the approval of Biogen’s Aduhelm™ (adacanumab) in June 2021—the first accelerated approval of an Alzheimer’s drug.

The FDA’s recent willingness to apply regulatory flexibility when reviewing neurology applications, as in the case of Qalsody and Aduhelm—and subsequently approve these drugs under the Accelerated Approval pathway, has resulted in a shift in the regulatory strategy being employed by sponsors. The strategy to utilize the Accelerated Approval pathway in neurology drug development programs may, in part, be driven by (1) the evolution of imaging and biomarkers that can potentially be used as surrogate endpoints, and (2) the openness of FDA to accept those surrogate endpoints.

While the use of the expedited pathway and the decision to use amyloid-beta as a surrogate endpoint reasonably likely to predict clinical benefit was surprising in the case of adacanumab, it has changed the regulatory landscape in neurology by setting precedent. Eisai and Biogen subsequently capitalized on that precedent by picking up another accelerated approval for Leqembi™ (lecanemab) for Alzheimer’s disease, and on June 9th the PCNS voted unanimously for full approval of Leqembi, based on results of the confirmatory trial.

It appears that the regulatory environment surrounding the amyloid-beta class of treatments for Alzheimer’s disease—primed by Aduhelm and Leqembi—continues to bear more fruit. In Eli Lilly’s May 3 press release, we saw the most encouraging results yet for a drug that targets amyloid beta. In a phase 3 trial of more than 1,000 people with early signs of Alzheimer’s disease, donanemab treatment resulted in a slowing of cognitive decline by 35% compared to placebo. It also resulted in 40% less decline in the ability to perform activities of daily living.

Interestingly in that trial, biomarkers were used in a relatively dynamic manner. Firstly, patients were prescreened using a predictive biomarker—plasma p-tau-181—thought to select for patients with both amyloid and tau tangle pathology (the 2 prominent pathologies in Alzheimer’s disease). Subsequently, the data revealed that those who start the trial with fewer tau tangles benefitted the most from donanemab. Incidentally, donanemab slowed (but did not stop) tangle growth, underscoring the benefit of treating earlier in the course of disease.

Secondly, the amyloid beta surrogate biomarker was used to inform clinicians on when to complete patients’ course of treatment with donanemab—by reaching a threshold of amyloid plaque clearance—a strategy that has not been employed in other Alzheimer’s disease trials testing antibodies against amyloid beta.

The National Academies of Sciences, Engineering, and Medicine (NASEM) convened a workshop in January 2023 to examine the FDA’s use of the Accelerated Approval program. There was renewed criticism of the FDAs approval of Aduhelm and a call to increase the transparency around FDA decision-making and its stance on surrogate endpoints. According to Peter Stein, director of the FDA’s Office of New Drugs, “We don’t have a simple formula or algorithm for evaluation of a potential surrogate for accelerated approval.” He went on to emphasize the limitations of using a correlation between the surrogate and established clinical outcomes that demonstrate clinical benefit.

There is clearly a need for a set of transparent standards for the utility of surrogate endpoints, both in therapeutic areas where accelerated approvals have traditionally been applied (oncology and HIV/AIDS) and in neurology. FDA guidance on surrogate endpoints is being developed. Under the Food and Drug Omnibus Reform Act of 2022 (FDORA), the FDA must issue 4 guidance documents related to accelerated approvals within 18 months of FDORA being enacted in December 2022; 2 of which will address surrogate endpoints.

As a wave of innovation continues to crash on the shores of neurological research, the interplay between novel therapeutics, surrogate endpoints, and regulatory flexibility has brought about tremendous progress in the field of neurodegenerative diseases. The FDA’s updated guidance on surrogate endpoints is likely to further accelerate the development of novel treatments based on the best available science.

References

  1. Ladanie A, Schmitt AM, Speich B, et al. Clinical trial evidence supporting US Food and Drug Administration approval of novel cancer therapies between 2000 and 2016. JAMA Netw Open. 2020;3(11):e2024406. doi: 10.1001/jamanetworkopen.2020.24406.

Muzamil Saleem, PhD
Associate Scientific Director, ProEd Regulatory
Muz is a trained neuroscientist with a diverse skillset, combining a 10-year neurology-focused research career, scientific consulting experience, and a 3-year tenure in healthcare equity research on Wall Street before joining ProEd Regulatory—all supported by a passion for written and visual scientific communication. Connect with Muz on LinkedIn.

The Rise of ChatGPT in the Pharma Industry

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

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.

 

PNAS Spearheads Effort to Streamline Authorship Transparency

Authorship is a hot topic in the scientific and medical publishing world. Who qualifies as an author? Who is the senior author? What are the responsibilities of the corresponding author? Opinions vary across disciplines and cultures. Whereas medical publications generally follow the recommendations of the International Committee of Medical Journal Editors (ICMJE; http://www.icmje.org/icmje-recommendations.pdf),1 academic publications may follow other guidance, or none at all. Is there a way to impose universal authorship criteria and quantify the work of authors so that their actual contributions can be tracked, giving them more than just their name on an article in the modern publish-or-perish environment?

A recent article by McNutt et al2 in Proceedings of the National Academy of Sciences of the United States of America (PNAS) seeks to create a framework for doing just that. As part of the global push toward greater transparency, with the goal of increasing integrity and trust in scientific publications, this article proposes that journals develop standardized authorship requirements and reporting, documented through ORCID identifiers (https://orcid.org) and the CRediT system (http://docs.casrai.org/CRediT).

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What the New ICMJE Requirement for Data Sharing Statements Really Means for Data Sharing

As of July 1, 2018, manuscripts submitted to International Committee of Medical Journal Editors (ICMJE)-member journals must be accompanied by a data sharing statement. What is the new requirement, how did it evolve, and what does it mean for data sharing?

In January 2016, the ICMJE proposed that authors of all clinical trial manuscripts published in member journals share de-identified individual-patient data (IPD) underlying their results within 6 months of publication.1 The proposal included data in tables, figures, appendices, and supplemental materials. The ICMJE invited comments on its proposal and a firestorm ensued. Although many individuals and groups applauded the ICMJE proposal, others raised legitimate concerns. Some were concerned about inappropriate analyses of data without statistical rigor, and authors were concerned about competition and losing control and/or credit for their work. Others voiced concerns about the practical aspects of how to share the huge amounts of data generated by some studies, particularly large, phase 3, randomized trials. Still others raised persistent concerns about patients’ right to privacy, particularly in the rare disease setting, where, despite de-identification efforts, patients still might be identifiable. Continue reading

Why Hire a Professional Medical Writer?

The updated Good Publication Practice guideline (GPP3) acknowledges the legitimate role of medical writers in helping authors with compliant, complete, and timely development of publications, “particularly when authors have limited time or lack knowledge of publication ethics and current publication and reporting guidelines.”1 Indeed, most authors (>84%) recently surveyed value the assistance provided by professional medical writers, particularly in editing manuscripts and ensuring conformity with reporting guidelines.2,3 In addition, emerging evidence suggests that the use of professional medical writers may enhance publication quality.1 So what impact does the medical writer really have on the quality of the publication? That is the question asked by William Gattrell and colleagues in their paper recently published in BMJ Open.4

Their cross-sectional study examined the relationship between medical writing support and the quality and timeliness of randomized controlled trial (RCT) reports. Completeness of the manuscript was assessed based on a predefined subset of 12 typically underreported items from the Consolidated Standards of Reporting Trials (CONSORT) checklist. Time from manuscript submission to editorial acceptance was also measured, as was the overall quality of written English as assessed by peer reviewers.

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Implications of ICMJE’s Data Sharing Proposal

ProEd-ICMJE-Data-Sharing-Proposal-PostOn January 20, members of the International Committee of Medical Journal Editors (ICMJE) announced a proposal that would require the authors of clinical trial publications to share the deidentified individual patient data that support their published results within 6 months of publication. Announced in an editorial published simultaneously in multiple medical journals, this proposal is based on the belief that authors have an “ethical obligation to responsibly share data generated by interventional clinical trials.” It also reflects the broader agenda of the ICMJE to foster greater transparency and reduce the potential for bias. This new requirement will likely go into effect in 2016 and will affect any clinical trial that enrolls patients beginning 1 year after ICMJE adopts the requirement.

This proposal makes a lot of sense in the interest of transparency, but what does it mean for clinical investigators involved in research and the companies that sponsor that research? To quote the ICMJE authors, “enabling responsible data sharing is a major endeavor that will affect the fabric of how clinical trials are planned and conducted and how their data are used.” Continue reading

GPP3: Is It a Better Guidance?

blog header linkedin - GPP3 v3 FINAL WP

The International Society for Medical Publication Professionals (ISMPP) recently released its latest guidance—GPP3, or Good Publication Practice 3. This is the first update of the ISMPP guidance since GPP2 was released in 2009. A steering committee first met to draft the guidance, and then ProEd colleagues, Laura McCormick, PhD; Heather Hlousek, and Jim Cozzarin, ELS, had the privilege, with 91 reviewers (from agencies and sponsors), to provide critical feedback before the new guidance was published in Annals of Internal Medicine.1

So, what are the important changes from GPP2? In addition to being more user-friendly than its predecessor—with an overall simplification of language and format, a new Guiding Principles section, and quick reference tables that address guidance on authorship criteria and common authorship issues—GPP3 also reflects some important updates and new elements1:

  • Updated International Committee of Medical Journal Editors (ICMJE) 2013 authorship criteria
  • Common issues regarding authorship
  • Improved clarity on author payment and reimbursement
  • Additional clarity on what constitutes ghost or guest authorship
  • Expanded information on the role and benefit of professional medical writers
  • Guidance for appropriate data sharing

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