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.


Regulatory Policy Watch: The FDA Is Taking Accelerated Approval Pathway Reforms Into Their Own Hands

On September 30, 2022, President Biden signed into law the reauthorization of the Prescription Drug User Fee Act (PDUFA VII), which will be in place for the next 5 years. Despite extensive bipartisan efforts to include reforms of the accelerated approval (AA) pathway as so-called “policy riders” in the bill, ultimately a “practically clean” version of the bill was signed.

While this news may have led to a sigh of relief for some sponsors, in the absence of formal measures to explicitly codify the FDA’s authority to tighten restrictions on AAs, it appears that the FDA has taken matters into its own hands.

FDA to ADC Therapeutics: “A randomized confirmatory phase 3 study must be well underway and ideally fully enrolled at the time of BLA filing.”

According to ADC Therapeutics, the FDA has “provided strong guidance that, for it to consider an accelerated approval path, a randomized confirmatory phase 3 study must be well underway and ideally fully enrolled at the time of any BLA filing.” As a result, ADC Therapeutics has taken a step back to reevaluate its experimental CD25-targeted antibody drug conjugate camidanlumab tesirine, which is being developed for patients with relapsed/refractory Hodgkin lymphoma. ADC had been planning to submit a BLA under the AA pathway based on a demonstrated ORR of 70.1% (95% CI, 60.9%-78.2%) in their phase 2, open-label, single-arm study in 117 heavily pretreated patients.

Because of the FDA’s guidance, ADC Therapeutics will no longer be submitting their BLA, and the future of the drug is uncertain. Enrollment for the planned confirmatory phase 3 trial for camidanlumab tesirine is estimated to take 2 years.

FDA regulatory policy shift for AA

A requirement for a fully enrolled confirmatory trial prior to granting AA represents a large shift in regulatory policy from the FDA.  

This move reflects the reforms advocated by the FDA’s Oncology Center of Excellence (OCE) to require confirmatory trials to be underway before an AA is granted. Such a requirement would likely lead to quicker confirmation of benefit and more timely withdrawal of an AA if clinical benefit is not confirmed. In support of this measure, studies have shown that among AAs that were withdrawn, the median time to withdrawal was 3.8 years if the confirmatory trial was ongoing at the time of AA, as compared with 7.3 years if such a trial had not been initiated.

Unfortunately, a requirement to have an ongoing, fully enrolled confirmatory trial at the time of filing for AA places a much greater burden on smaller drug development companies. Smaller companies in particular may depend on revenue from drugs marketed under the AA pathway to finance phase 3 confirmatory studies. Greater restrictions on the AA pathway may force smaller companies, such as ADC, to scrap certain drug development programs entirely.

The OCE has been vocal about improving the quality and efficiency of the AA pathway, and it does not appear to be waiting around for legislation to follow through on instituting more requirements for granting AAs and in rapidly withdrawing AA indications that fail to confirm benefit in subsequent phase 3 trials.

Angela W. Corona, PhD
Scientific Director, Scientific Services
Angela is responsible for helping sponsors navigate complex regulatory communications, such as FDA advisory committee meetings. She develops clinical and regulatory strategy along with high-quality scientific and medical content across a wide range of therapeutic and drug development areas. Angela received her PhD in Neuroscience from The Ohio State University and completed her postdoctoral training at Case Western Reserve University. Connect with Angela on LinkedIn.


  1. Fashoyin-Aje LA, Mehta GU, Beaver JA, Pazdur R. The on- and off-ramps of oncology accelerated approval. N Engl J Med. 2022;387(16):1439-1442.

FDA Sets High Bar for Real-World Evidence in Rare Diseases

Real-world data (RWD) can be used to create historical control groups for clinical trials in rare diseases where a randomized controlled trial (RCT) is not feasible. But what happens when the US Food and Drug Administration (FDA) doesn’t accept it?

Since passage of the 21st Century Cures Act in 2016, FDA has promoted the use of real-world evidence (RWE) to increase the efficiency of clinical research. However, according to
FDA’s 2018 RWE framework, the use of RWE is primarily restricted to evaluating safety
(eg, monitoring postmarketing safety). It can only be used in limited circumstances to inform decisions about effectiveness.

When it comes to regulatory decisions about product effectiveness, FDA’s framework suggests that RWE can be used to support changes to labeling about product effectiveness, including adding or modifying an indication, such as a change in dose, dose regimen, or route of administration, adding a new population, or adding comparative effectiveness data.

So, where does that leave sponsors who want to compare the results of a single-arm clinical trial to a real-world historical control arm to demonstrate the effectiveness of a new product? Unfortunately, the FDA has set a very high bar.

Regulatory “Fitness” in Rare Disease Clinical Trials

At a joint FDA-National Institutes of Health workshop in May 2022, titled “Regulatory Fitness in Rare Disease Clinical Trials,” Katie Donohue, Director of the Division of Rare Diseases and Medical Genetics in the Center for Drug Evaluation & Research, said that the challenges facing sponsors attempting a single-arm approach to develop a first therapy for a rare disease are so daunting that development programs only “work when you are very lucky.” In particular, she pointed out that single-arm studies are vulnerable to changes in rare disease natural history.

Changes in natural history, response assessment, and standard-of-care therapy can have a dramatic effect on time-to-event endpoints such as overall survival (OS). So, for a single-arm trial, FDA recommends concrete, confirmed endpoints “like an x-ray or blood test.”

These comments highlight the strong preference FDA has for RCTs, in general, and even for rare diseases, where it is often extremely challenging to conduct an RCT with sufficient statistical power to demonstrate effectiveness.

FDA Has Set the Bar Very High

FDA’s draft guidance, titled Considerations for the Use of Real-World Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological Products, acknowledges the potential utility of using RWD in interventional studies, including “to serve as a comparator arm in an external control trial.” However, the guidance focuses heavily on the use of RWD/RWE in non-interventional studies, such as observational cohort studies and case control studies that evaluate the safety and effectiveness of a product in routine medical practice and are not governed by a research protocol.

Although FDA is not opposed to the idea to using RWD to construct historical control groups also referred to as an external control arm – it is highly critical of that approach as the basis for regulatory approval of a novel drug.

Recently, Y-mAbs Therapeutics found this out the hard way. In collaboration with Memorial Sloan Kettering Cancer Center (MSKCC), Y-mAbs has developed a targeted radiolabeled antibody called 131I-omburtamab for the treatment of neuroblastoma that has metastasized to the central nervous system (CNS). This ultrarare pediatric indication affects only about 20 patients per year in the United States, and there are no approved therapies.

With traditional treatment approaches – surgery, radiotherapy (RT), and chemotherapy – most patients only survive a few months after diagnosis of CNS metastases. For about one third of patients who survive long enough to receive 2 or 3 treatment modalities, median survival is about 15 months. So, the clinical team at MSKCC, led by Dr. Nai-Kong Cheung and Dr. Kim Kramer, developed 131I-omburtamab, an anti-B7-H3 antibody, which they inject directly into the cerebrospinal fluid via an Ommaya catheter, as an adjunct to standard therapy. The goal is to eradicate residual tumor cells and increase the chance of achieving a cure.

The team at MSKCC has been studying the safety and effectiveness of 131I-omburtamab in this poor-prognosis patient population since 2004. In that timeframe, they have treated more than 100 children with CNS neuroblastoma, of whom about 40% have survived more than 8 years. Their treatment protocol demonstrated a median OS of 51 months, a milestone that clinical experts consider quite extraordinary.

Fast Forward to 2015

In 2015, Thomas Gad, whose daughter was successfully treated at MSKCC for CNS neuroblastoma, founded Y-mAbs Therapeutics to further develop 131I-omburtamab and get it approved in the US, so other children could have access to this potentially lifesaving drug.

To demonstrate the effectiveness of 131I-omburtamab, the company conducted its own single-arm multicenter trial in 50 patients, principally to confirm the results from the single-institution MSKCC trial and demonstrate objective responses to the drug. Given the rarity of this indication, an RCT was not feasible.

Y-mAbs then set out to obtain patient-level data from children with CNS neuroblastoma treated outside MSKCC and construct an external control arm for comparison with the MSKCC trial population. They succeeded in identifying only one suitable database, a neuroblastoma registry in Germany, and they were able to extract patient-level data from 120 patients who had a first recurrence of neuroblastoma in the brain. In collaboration with the FDA, Y-mAbs designed the comparative analysis using a propensity score model.

After carefully balancing the intensity of standard treatment with surgery, RT, and chemotherapy (modality group 2), the Y-mAbs biometrics team identified a cohort of 34 patients from the external control arm that they could compare to 89 patients in the MSKCC study. A comprehensive propensity score model that controlled for potential confounding factors demonstrated a 42% improvement in OS (hazard ratio = 0.58) compared with the external control arm. Sensitivity analyses showed a consistent treatment effect (hazard ratios ranged from 0.42 to 0.66) in favor of 131I-omburtamab.

Y-mAbs also went one step further, restricting the analysis to only patients in first recurrence, adjusting the index dates to control for immortal time bias, and removing patients from the external control arm treated prior to 1997. That analysis, which represents the best possible match between the populations, showed a 52% improvement in OS (Figure 1).

Figure 1.   Overall survival in patients in modality group 2 treated at first recurrence comparing index dates A vs D and excluding NB90 from external control arm

These data, along with supportive data from the multicenter trial, were the basis for the Y-mabs Biologics License Agreement filed in March 2022. However, after careful review of the data, FDA’s Oncology Division concluded that the external control arm is “not fit for purpose.” FDA argued that limitations of the data and multiple sources of potential bias resulted in a large degree of uncertainty regarding whether the observed OS difference was due to 131I‑omburtamab or differences between the populations, or a combination of these factors. FDA also had doubts about the objective response data.

FDA Oncologic Drugs Advisory Committee (ODAC) Meeting

At the ODAC meeting on October 28, 2022, the FDA presented its case that the 2 populations were not comparable, primarily because of differences in treatment intensity and era of therapy. They pointed out that none of the patients in the external control arm received craniospinal irradiation, a form of RT perceived to be more effective than the standard focal or whole-brain RT given to the German patients. However, there are no published studies to show that it is more effective in neuroblastoma. The FDA also presented evidence that clinical outcomes for CNS neuroblastoma have improved over time. Consequently, FDA restricted its analysis to only those patients in the external control arm who were treated from 2004 to 2015, the time period corresponding to the MSKCC study.

After adjusting for all these potential confounders, including immortal time bias, the FDA analysis showed a hazard ratio of 1.0, suggesting no OS benefit.

Ultimately, the committee voted unanimously that the Applicant had not provided sufficient evidence to conclude that 131I-omburtamab improves OS in the proposed indication. The committee wanted to see more data. Unfortunately, that may not be feasible.

This case sends a strong message regarding the rigor of data that FDA expects when establishing effectiveness based on a time-to-event endpoint in a single-arm trial with comparison to an external control arm, even in a rare disease where it exercises regulatory flexibility. The consequence of this ODAC decision means that sponsors will face a high bar when attempting to demonstrate that an external control arm is “fit for purpose.”

Jeff Riegel, PhD
SVP, Scientific Communications, ProEd Regulatory
Jeff combines his scientific expertise in molecular biology and immunology with more than 25 years of global healthcare agency experience guiding medical and regulatory communication strategies for biopharma companies. Jeff helps clients prepare for FDA Advisory Committee meetings and other health authority interactions. Connect with Jeff on LinkedIn.

The Alzheimer’s Conundrum

The United States is facing an avalanche of Alzheimer’s disease (AD). An estimated 12.7 million Americans over the age of 65 are projected to suffer from AD dementia by 2050,1 and yet, despite more than 30 years of intensive research, we have yet to develop a drug that provides a clinically meaningful slowing in cognitive decline. There are only 6 AD drugs currently approved in the US. Five of these are symptomatic treatments, such acetylcholinesterase drugs for mild AD and the N-methyl-D-aspartate (NMDA) receptor antagonist memantine, which is used as an add-on or second-line therapy in more severe cases. Aduhelm, a beta-amyloid monoclonal antibody (mAb) therapy, represented the sixth US Food and Drug Administration (FDA) approval in AD and is the first to target the underlying pathology of the disease. However, the beta-amyloid mAbs have failed to live up to the promise of delivering a curative, disease-modifying drug (DMD). We examine directions in research and development that contribute a set of diverse pathological targets—illuminating Alzheimer’s disease as a conundrum that will likely be solved with a multifactorial treatment approach.

The Alzheimer’s Conundrum

So, why have effective AD therapeutics been so elusive? A key factor is the cavernous deficiency in our pathologic understanding of AD. In the amyloid hypothesis, plaques composed of toxic beta-amyloid and phosphorylated tau protein are hypothesized to cause neurodegeneration leading to cognitive decline. This hypothesis has dominated the last 20 years of AD research and carries with it a storm of controversy, to the extent that two camps have formed (those who support the amyloid hypothesis, and those who don’t). At issue is the failure of a long line of drugs targeting beta-amyloid in clinical trials dating back to 2003. Interviews with multiple scientists suggest that any research that fell outside of the amyloid field was suppressed,2 likely slowing progress in the field. In addition, the dire unmet need for AD therapeutics has put a strain on translational research, such that biotech companies have scrambled to move drugs into clinical trials, perhaps before the science was fully baked.

The pathology of AD, best represented as a continuum (Figure 1),1 presents another challenge. Researchers and clinicians believe that therapeutic intervention stands the best chance of success in patients with mild cognitive impairment, but without a reliable biomarker, it can be challenging to identify an appropriate patient population for clinical studies.

Figure 1. Alzheimer’s disease continuum.

The pathological changes in the brain that cause the first noticeable symptoms of AD—related to memory, language, and cognition—are thought to start 2 decades or more prior.3-10 During this asymptomatic phase there may be measurable changes in a biomarker that could indicate future progression to clinical AD. Hence, there is a crucial need for a biomarker sensitive enough to detect AD in early stages. To date, the best available biomarker of AD is the assessment of abnormal beta-amyloid deposits in the brain by positron emission tomography (PET) imaging.

The Lumipulse® beta-amyloid test (Fujirebio Diagnostics) was FDA approved earlier this year and could potentially substitute for the use of PET scans to detect amyloid pathology; however, the test requires collection of cerebrospinal fluid (CSF), which is an unpleasant procedure. The search for a minimally invasive, blood-based biomarker has been at the center of a fervent research effort over the past decade.11 The PrecivityAD® is a blood test developed by C2N Diagnostics that has been shown to be 81% accurate in predicting the level of beta-amyloid in the brain; however, it is not yet FDA approved.

The First Drug to Address the Underlying Biology of Alzheimer’s Disease

The accelerated approval of aducanumab (Aduhelm™), an antibody that binds to and clears beta-amyloid plaques in the brain, in 2020 was a landmark in the treatment of AD, signaling the first new drug in 18 years. Aduhelm was studied in two large, randomized, controlled trials in patients with mild cognitive impairment and evidence of amyloid pathology by PET imaging. However, both trials were prematurely stopped for futility. The first trial (EMERGE) ultimately met its primary endpoint—patients on high-dose aducanumab showed a significant slowing of cognitive decline from baseline. The second trial (ENGAGE) did not meet its primary endpoint; however, patients from this trial who received sufficient exposure to high-dose aducanumab showed efficacy results supporting the findings of EMERGE. Both trials also showed a statistically significant, dose-dependent decrease in beta-amyloid and phosphorylated tau protein by PET imaging, which was the basis for accelerated approval.

Controversy ensued when an independent panel of scientific and clinical experts was assembled at an FDA Advisory Committee meeting to deliberate over the approval of Aduhelm. The committee advised unanimously against the approval of aducanumab. Despite convincing evidence that Aduhelm effectively removes beta-amyloid plaques from the brain, experts argued that the two large phase 3 clinical trials— one positive and one negative—did not conclusively demonstrate a slowing in cognitive decline.

The approval of Aduhelm has not quelled any of the controversy surrounding the amyloid hypothesis. There remains a significant unmet need for disease-modifying AD therapeutics that result in a slowing—or indeed a reversal—of cognitive decline. The following post in this Alzheimer’s series will explore the next wave of AD drug development. We will place our focus beyond aberrant amyloid and tau protein pathology, and examine a multifactorial set of disease mechanisms, including inflammatory cascades, gut-brain signaling, and axonal transport.

Muzamil Saleem, PhD
Associate Scientific Director, ProEd Regulatory
Muz is a trained neuroscientist with a diverse skillset, combining a ten-year neurology-focused research career, scientific consulting experience and a three-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.


  1. 2022 Alzheimer’s disease facts and figures. Alzheimers Dement. 2022;18(4):700-789.
  2. Begley S. The maddening saga of how an Alzheimer’s ‘cabal’ thwarted progress toward a cure for decades. STAT. 2019.
  3. Quiroz YT, Zetterberg H, Reiman EM, et al. Plasma neurofilament light chain in the presenilin 1 E280A autosomal dominant Alzheimer’s disease kindred: a cross-sectional and longitudinal cohort study. Lancet Neurol. 2020;19(6):513-521.
  4. Barthelemy NR, Li Y, Joseph-Mathurin N, et al. A soluble phosphorylated tau signature links tau, amyloid and the evolution of stages of dominantly inherited Alzheimer’s disease. Nat Med. 2020;26(3):398-407.
  5. Villemagne VL, Burnham S, Bourgeat P, et al. Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurol. 2013;12(4):357-367.
  6. Reiman EM, Quiroz YT, Fleisher AS, et al. Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: a case-control study. Lancet Neurol. 2012;11(12):1048-1056.
  7. Jack CR, Jr., Lowe VJ, Weigand SD, et al. Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer’s disease: implications for sequence of pathological events in Alzheimer’s disease. Brain. 2009;132(Pt 5):1355-1365.
  8. Bateman RJ, Xiong C, Benzinger TL, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med. 2012;367(9):795-804.
  9. Gordon BA, Blazey TM, Su Y, et al. Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. Lancet Neurol. 2018;17(3):241-250.
  10. Braak H, Thal DR, Ghebremedhin E, Del Tredici K. Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years. J Neuropathol Exp Neurol. 2011;70(11):960-969.
  11. Shi L, Baird AL, Westwood S, et al. A Decade of Blood Biomarkers for Alzheimer’s Disease Research: An Evolving Field, Improving Study Designs, and the Challenge of Replication. J Alzheimers Dis. 2018;62(3):1181-1198.

Common Protocol Template—Streamlining Protocol Implementation

Study protocols are required for every clinical trial. Approximately 20,000 are submitted and posted to every year1—each one different. The format and core content can vary from sponsor to sponsor, costing the US Food and Drug Administration (FDA) time and resources to interpret, review, and ultimately, approve each uniquely complex protocol. This process, as it stands, slows down progress for new drug development. Clearly, there is a need to accelerate the pace at which protocols are approved so that new clinical studies can be initiated. In a world where technology continues to offer a platform for efficiency and accuracy, the development of the Common Protocol Template (CPT) is a welcome addition to the medical field. Common Protocol Templates can lead to faster review time, simplified trial startup, and prompt execution of clinical trials. Although the use of a CPT is not required for all new clinical trials, it is only a matter of time before its use becomes commonplace in drug development. Continue reading