Alzheimer’s disease (AD) is characterized histologically by amyloid-β (Aβ) deposition in the brain. Immunotherapies targeting Aβ clearance have become a leading treatment strategy. Although these therapies effectively reduce cerebral Aβ burden, their cognitive benefits remain modest during the trial period. This review systematically assesses the extent of Aβ clearance by immunotherapies and its related cognitive outcomes, focusing on whether cognitive benefits increase over time. We refine a model of the “lag effect” between plaque clearance and cognitive benefit, which is potentially influenced by clearance rate, treatment duration, disease stage, genetic factors, and aging. We also discuss the underlying biological mechanisms and potential neuroprotective targets. Future research should prioritize long-term studies, early intervention, personalized therapies, and combination approaches addressing multiple pathological pathways. Given limited short-term cognitive gains, optimizing outcomes will require tailoring treatments to individual patient factors—including genetics, disease progression, and aging—to minimize side effects and enhance long-term cognitive function.

BackgroundInvestigations into the role of traditional Chinese medicine (TCM) pharmacotherapy in the early stage of the COVID-19 pandemic in real clinical circumstances are necessary as we reflect on the past. PurposeTo observe the effectiveness of TCM on real-world clinical outcomes of COVID-19 patients during surges of SARS-CoV-2 alpha and delta variants. MethodsA retrospective cohort design was used. Data was collected from 9 clinical sites across mainland China. 2021 confirmed COVID-19 patients admitted between January 1, 2020 and January 17, 2022 were screened for inclusion. Exposure was TCM pharmacotherapy (prescriptions or patent drugs). Primary outcomes include hospital discharge rate and negative conversion rate of SARS-CoV-2 infection. Secondary outcomes include the length of hospital stay, negative conversion time of the SARS-CoV-2 virus, clinical recovery rate on day 7 of hospitalization, and the rate of worsened computed tomography presentations on day 14 of hospitalization. The Cox proportional hazards regression model and propensity score matching were used for statistical analysis. ResultsThree cohorts were defined for convenience of analysis. The complete cohort included 1747 COVID-19 patients (medium [IQR] age, 40[30-50] years; 1017[58.2%] male). Analysis of the complete cohort found TCM pharmacotherapy, aged 65 or younger, living in South China, and early treatment were protective factors of hospital discharge, and living in South China was a protective factor of negative conversion of the virus. Analysis of the early treatment cohort (of 1066 patients) found TCM pharmacotherapy, aged 65 or younger, and living in South China were protective factors of hospital discharge, and living in South China was a protective factor of negative conversion. Analysis of the matched-pair cohort (of 462 mild-to moderate COVID-19 patients) found TCM group had a higher discharge rate (P<0.001), a shorter hospital stay (14d v.s. 16d, P<0.001) and a higher clinical recovery rate (83.3% v.s. 31.0%, P<0.001) compared to the combined treatment group. ConclusionsTCM pharmacotherapy increased discharge rate, clinical recovery rate, and shortened hospital length of stay in mild-to-moderate COVID-19 patients during surges of the alpha and delta variants.

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

BACKGROUND:The Food and Drug Administration (FDA)'s Accelerated Approval (AA) pathway has increasingly used to authorize market approval of new drugs amid controversy. The present study aims to inform the most recent data on the strength of clinical evidence supporting such approvals. METHODS:Evidentiary characteristics of pre-approval pivotal clinical studies and regulator-required post-approval confirmatory studies supporting AAs between 2015 and 2022 were extracted from publicly available FDA documents. Descriptive analyses were conducted for each of the characteristic including study design, study phase, primary endpoint, number of participants, and magnitude of effect. Trends of these characteristics over time were documented and accounted for class of drugs, application type, novelty, orphan status, and oncology/non-oncology indications. RESULTS:During 2015-2022, 156 drug-indication pairs received AA. To support these AAs, 77% of pre-approval pivotal trials employed single-arm designs, and 22% were phase I trials, with a median of 92 participants (IQR, 45-125); 61% of post-approval confirmatory studies were required by FDA to use randomized controlled design, 25% to use clinical endpoints, and 33% specified the number of participants requirement. During the 8-year observation period, the pairs approved via AA pathway almost tripled from 20 (2015-2016) to 59 (2019-2020) and fell to 36 (2021-2022); the corresponding proportion to all new drug approvals showed the same trend. Single-arm pre-approval pivotal studies increased from 55% (2015-2016) to 91% (2019-2020) and fell to 69% (2021-2022), while the median number of participants decreased from 106 (2015-2016) to 59 (2019-2020) and rose to 106 (2021-2022). Randomized controlled post-approval confirmatory studies decreased from 75% (2015-2016) to 42% (2019-2020) and rebounded to 75% (2021-2022), while those using surrogate endpoints increased from 50% (2015-2016) to 72% (2021-2022). Analyses adjusting for drug class, application type, novelty, orphan status, and oncology/non-oncology showed similar results. CONCLUSIONS:The number of drug-indication pairs receiving AA increased sharply during 2015-2016 to 2019-2020 but fell in 2021-2022. Meanwhile, the strength of clinical evidence supporting FDA's AAs appeared to decline from 2015 to 2020 but seems to have improved in 2021-2022. Measures should be taken to further improve the strength of evidence in Accelerated Approvals.

The application of model-informed drug development (MIDD) has revolutionized drug development and regulatory decision making, transforming the process into one that is more efficient, effective, and patient centered. A critical application of MIDD is to facilitate dose selection and optimization, which play a pivotal role in improving efficacy, safety, and tolerability profiles of a candidate drug. With the surge of interest in small interfering RNA (siRNA) drugs as a promising class of therapeutics, their applications in various disease areas have been extensively studied preclinically. However, dosing selection and optimization experience for siRNA in humans is limited. Unique challenges exist for the dose evaluation of siRNA due to the temporal discordance between pharmacokinetic and pharmacodynamic profiles, as well as limited available clinical experience and considerable interindividual variability. This review highlights the pivotal role of MIDD in facilitating dose selection and optimization for siRNA therapeutics. Based on past experiences with approved siRNA products, MIDD has demonstrated its ability to aid in dose selection for clinical trials and enabling optimal dosing for the general patient population. In addition, MIDD presents an opportunity for dose individualization based on patient characteristics, enhancing the precision and effectiveness of siRNA therapeutics. In conclusion, the integration of MIDD offers substantial advantages in navigating the complex challenges of dose selection and optimization in siRNA drug development, which in turn accelerates the development process, supports regulatory decision making, and ultimately improves the clinical outcomes of siRNA-based therapies, fostering advancements in precision medicine across a diverse range of diseases.

This paper designs a fuzzy adaptive control approach for the Continuous Stirred Tank Reactors (CSTRs) with full state constraints and actuator faults. Using the backstepping design technique and Integral barrier Lyapunov function (IBLF), the full state constraints are handled in the design process of the adaptive controller. Besides, there exist unknown internal dynamics in the CSTRs system, which are identified by the fuzzy logic system. It is shown that the constraints are not overstepped and the CSTRs operate reliably is ensured, despite the presence of system nonlinearities, stuck faults and loss of control effectiveness. Based on the Lyapunov stability theory, the proposed approach can ensure that all the signals of the CSTRs system are globally bounded. Finally, the simulation results on CSTRs are showed to reveal the availability of the developed control scheme.

Abstract. Tumor chemoprevention and treatment are two approaches aimed at improving the survival of patients with cancers. An ideal anti-tumor drug is that which not only kills tumor cells but also alleviates tumor-causing risk factors, such as precancerous lesions, and prevents tumor recurrence. Chinese herbal monomers are considered to be ideal treatment agents due to their multi-target effects. Astragaloside has been shown to possess tumor chemoprevention, direct anti-tumor, and chemotherapeutic drug sensitization effects. In this paper, we review the effects of astragaloside on tumor prevention and treatment and provide directions for further research.

The Center for Pharmacometrics and Systems Pharmacology (CPSP) at the University of Florida has been engaging stakeholders from industry and regulatory agencies in recent years to seek input on the requirements and expectations for next generation pharmacometricians in the workplace. The objective of this article is to share our joint perspective on identified key skills with the broader pharmacometrics community in order to initiate a collective consensus building process on how to best develop them. Pharmacometrics has evolved from a descriptive science to an applied science that is increasingly used in all phases of drug development over the last decades. Today's application for accelerating and streamlining drug development is referred to as model-informed drug development (MIDD) or, more broadly, model-informed drug discovery and development (MID3).1, 2 Due to the increasing application of MID3 approaches, rapid emergence of new data analysis and computational methods as well as increasing complexity of drug development and regulatory evaluation processes, demands for and toward pharmacometricians have been evolving over the years as well.3-5 It is no longer sufficient to master a certain tool or technical skill. Instead, these skills need to be applied in a team-based environment to solve a drug development problem. Strong technical skills and the ability to identify when pharmacometrics analyses can be used to answer a particular question are of course the foundation for every pharmacometrician. In addition to these foundational technical skills, it is our firm belief that a successful pharmacometrician should ideally: (1) be an effective communicator, (2) be able to think strategically, and (3) be able to influence team-based decisions, as outlined in Figure 1. A solid foundation of scientific knowledge (e.g., basic pharmacokinetic/pharmacodynamic [PK/PD] and pharmacology concepts) and technical pharmacometrics skills is the basis for being able to successfully apply MID3 approaches.6 Foundational technical skills include but are not limited to nonlinear mixed effects (NLME) PK/PD modeling, mechanistic PK/PD modeling, including physiologically-based pharmacokinetic (PBPK), quantitative systems pharmacology (QSP) modeling and clinical trial simulations, as shown in Table 1. We believe that a strong technical skill set comprises familiarity with pharmacometric methods, software, and programming language(s), the ability to critically assess the scientific validity of a model and its associated parameter values, as well as knowledge in pathophysiology, PKs, pharmacology, toxicology, statistics, and mathematics. These foundational technical skills are essential to influence decision-making in drug discovery and development as well as during regulatory review. With the increased use of MID3 approaches in drug discovery and early drug development,1 familiarity with emerging sciences and technologies, such as machine learning, artificial intelligence, and models relating structural properties of chemical compounds to potency/safety/PK, are becoming more important. As a consequence, the spectrum of required technical pharmacometrics skill sets has widened even further and two questions arise. To which extent do pharmacometricians need to cover the entire width of the spectrum? Is there a need for specialization among pharmacometricians? Strategic thinking entails the ability to anticipate both challenges and opportunities and to plan a course of action accordingly.8 Strategic thinking in the context of drug development requires a thorough understanding of the drug development process, applicable regulations and guidelines, and appreciation of organizational constraints (cost, time, value/risk). Obtaining this understanding requires time and is often the reason why junior pharmacometricians have difficulty leveraging MID3 approaches to streamline and accelerate drug development. Exposing junior pharmacometricians to drug development problems already during their training program (e.g., during internships in industry/regulatory agencies or joint research projects with industry/regulatory agencies) is consequently important. On the other hand, MID3 approaches provide an opportunity to facilitate strategic thinking because they allow for the integration of complex knowledge from multiple sources, explore different scenarios by quantifying assumptions, and prospectively choose the one that best meets the organization's goal (e.g., most pragmatic or has the highest probability of success). The combination of technical and strategic skills ensures that pharmacometricians can identify critical questions in drug development programs that may be answered using MID3 approaches. Strategic skills as well as the ability to influence and negotiate are essential for identifying critical questions within and across project teams, provide teams with an option for decision making based on modeling and simulation, and conveying the solutions in a manner that engages the audience. The ability to systematically integrate information and extrapolate beyond what has already been studied holds great potential for influencing decisions in drug development and regulatory approval. To broaden the impact that pharmacometricians can have on a final decision, it is important for her/him to be involved in all phases of drug discovery and development, including prospective study design, execution of the study, analyzing the data, and performing simulations for the next trial(s) along with making go/no-go decisions. Establishing this mindset early on in focused teaching and research curricula that integrate drug discovery and development with pharmacometrics is consequently beneficial. At the same time, it is important to remember that decision making is a complex process, which is affected by evidence, beliefs, assumptions and bias, a combination recurrent in common judgments. Biases in judgments reveal heuristics in our thinking under uncertainty, which can lead to severe and systematic errors. Decision making is also impacted by the way a scenario is framed. For example, a 90% chance of success would likely be perceived more favorably than a 10% risk of failure, although they are mathematically the same.9 We believe that pharmacometricians must understand this interplay and continuously educate themselves on how to maximize the impact of MID3 with the overall goal in mind (i.e., to accelerate and streamline drug development and ultimately improve patient care). At the same time, they must be willing to make trade-offs, when necessary (i.e., be adaptive and pragmatic to achieve consensus amongst team members). On top of the general increase in demand for pharmacometricians, there is an imbalance among supply, demand, and professional working opportunities between different regions of the world, resulting in geographic and academic brain drain.10 Particularly the latter poses an imminent threat to the pharmacometrics community because if this trend continues, we will soon reach a point where we will no longer have a sufficient number of academicians, particularly at the Associate and Full Professor level, that are able to train next generation pharmacometricians. To overcome these challenges, a general rethinking of traditional, siloed “business models” toward joint efforts between academia, industry, and regulatory agencies will be required. These efforts can be established at various levels, ranging from loose affiliations, such as adjunct appointments or internship opportunities for students and trainees, to structured partnerships with a dedicated logistic, financial, educational, and research infrastructure support. The latter would allow to overcome limitations of individual stakeholders (e.g., limited time for developing concepts or platform models outside the direct drug development pipeline or teaching drug development without having worked in the industry) and provide planning security (e.g., proactive workforce pipeline development, PhD and postdoctoral support for the duration of the training program, or increased utilization of large-scale databases for disease platform model development) for all parties involved. These joint efforts would also allow for the development of applied training modules, where stakeholders bring their individual strengths to the table (i.e., concepts and hands-on software training [academia], drug development context and possibly data [industry], regulatory context [regulators]). Combining forces would also allow us to stay abreast with the rapidly evolving drug development and regulatory evaluation landscape and offer training for new modalities, concepts, and analysis approaches in a timely fashion. Ideally, these partnerships would be interdisciplinary in nature to enable a broader vision to problems and ultimately spark innovation by crossing traditional knowledge boundaries. A transdisciplinary approach that integrates, for example, PBPK, machine learning, and artificial intelligence or pharmacometrics and pharmacoepidemiology, would also further a mindset of constant learning and collaboration, which is key to success in team-based environments. To facilitate the interactions, we collectively composed a list of proposed teaching and training activities needed for developing technical, strategic, as well as communication and influencing skills (Table 1). We recognize that this list, although too lengthy for any single PhD or postdoctoral fellowship program, is not all-encompassing and that the outlined activities should be tailored toward the individual trainee's educational background and working experience. We also recognize that training activities in academia may have to be complemented by downstream activities. For example, two-way sabbaticals may allow working professionals from industry or regulatory agencies to retool in academia, whereas academicians could stay abreast with latest advances in drug discovery, development, and regulatory evaluation while spending time in the industry or at the agency. Finally, we do not intend to infringe on individual faculty's freedom to train their students as they see fit, dismiss previous curricula proposal,6 or suggest that academia should take sole responsibility for the proposed teaching and training activities. We rather intend to use this proposal to spark a broader conversation among stakeholders in the pharmacometrics arena to collectively build consensus on key skills and outline viable avenues for how to best develop them. As such, we invite all stakeholders to join this conversation and welcome any constructive feedback on our proposal. The authors would like to thank Benjamin Weber for his input into the manuscript. No funding was received for this work. The authors declared no competing interests for this work.

BackgroundThis proof-of-concept retrospective case study investigated whether patient-reported outcomes (PRO) instruments, designed to capture symptomatic adverse event data, could identity a known exposure-response (ER) relationship for safety characterized in an original FDA analysis of an approved anti-cancer agent. PRO instruments have been designed to uniquely quantify the tolerability aspects of exposure-associated symptomatic adverse events. We explored whether standard ER analyses of clinician-reported safety data for symptomatic adverse events could be complemented by ER analysis using PRO data that capture and quantify the tolerability aspects of these same symptomatic adverse events.MethodsExposure-associated adverse event data for diarrhea were analyzed in parallel in 120 patients enrolled in a clinical trial using physician reported Common Terminology Criteria for Adverse Events (CTCAE) and patient-reported symptomatic adverse event data captured by the National Cancer Institute's (NCI) PRO Common Terminology Criteria for Adverse Events (PRO-CTCAE) instrument. Comparative ER analyses of diarrhea were conducted using the same dataset. Results from the CTCAE and PRO-CTCAE ER analyses were assessed for consistency with the ER relationship for diarrhea established in the original NDA using a 750-patient dataset. The analysis was limited to the 120-patient subset with parallel CTCAE and PRO-CTCAE assessments.ResultsWithin the same 120-patient dataset, ER analysis using dense, longitudinal PRO-CTCAE-derived data was sensitive to identify the known ER relationship for diarrhea, whereas the standard CTCAE based ER analysis was not.ConclusionsER analysis using PRO assessed symptomatic adverse event data may be a sensitive tool to complement traditional ER analysis. Improved identification of relationships for safety, by including quantification of the tolerability aspect of symptomatic adverse events using PRO instruments, may be useful to improve the sensitivity of exposure response analysis to support early clinical trial dosage optimization strategies, where decision making occurs within limited small patient datasets.

Clinical trials have demonstrated the benefit of PD-1/PD-L1 blocking antibodies for the treatment of patients with advanced non-small cell lung cancer (NSCLC) in defined patient populations that often exclude patients with moderate or severe hepatic or renal impairment. We assessed the association between overall survival (OS) and baseline organ function in patients with advanced NSCLC treated with PD-1/PD-L1 blocking antibodies in real-world data (RWD; patient-level data from electronic health records) and pooled clinical trial data submitted to the US Food and Drug Administration (FDA). The Kaplan-Meier estimator was used to estimate OS in different subgroups based on organ function. Unadjusted and adjusted Cox proportional hazards models were used to estimate the association between OS and organ function. In this hypothesis-generating study, baseline renal impairment did not appear to be associated with OS, while patients with baseline liver impairment had shorter OS. RWD provided information on a broader range of renal and hepatic function than was evaluated in clinical trials and hold promise to complement trial data in better understanding populations not represented in clinical trials.

Introduction:Aducanumab was approved in 2021 by the US Food and Drug Administration (FDA) under the accelerated approval pathway. Since then, there have been many misconceptions about the approval decision despite multiple publications from the FDA to explain the rationale. Methods:Even though the FDA's final decision was accelerated approval, the Office of Clinical Pharmacology recommended regular/full approval based on its own analyses. Exposure-response analyses were conducted to quantify the relationship between aducanumab longitudinal exposure and responses (standardized uptake values ratios for amyloid beta and various clinical endpoints) in all clinical trials. To explain the difference between aducanumab and other compounds with negative results in the past, publicly available data were combined with the aducanumab data to demonstrate the relationship between amyloid reduction and clinical endpoint change across multiple compounds with similar mechanism of action. The probability to observe the overall positive findings in the aducanumab program was quantified under the assumption that aducanumab is ineffective. Results:Positive exposure-response (disease progression) relationship for multiple clinical endpoints from all clinical trials was identified. Positive exposure-amyloid reduction relationship was established. Consistent amyloid reduction-clinical endpoint change relationship across multiple compounds was observed. If aducanumab is assumed to be ineffective, it is extremely unlikely we would observe the overall positive findings in the aducanumab program. Conclusion:These results provided convincing evidence to support aducanumab's effectiveness. In addition, the observed effect size in the studied patient population represents a clinically meaningful benefit given the magnitude of disease progression within the trial duration. Highlights:Totality of evidence supports the Food and Drug Administration (FDA)'s approval decision for aducanumab.Different opinions were clearly explained in the FDA's public reviews from different disciplines.Readers are encouraged to read the FDA's reviews to understand the FDA's rationale to approve aducanumab.

Remdesivir (RDV) is the first drug approved by the US Food and Drug Administration (FDA) for the treatment of coronavirus disease 2019 (COVID-19) in certain patients requiring hospitalization. As a nucleoside analogue prodrug, RDV undergoes intracellular multistep activation to form its pharmacologically active species, GS-443902, which is not detectable in the plasma. A question arises that whether the observed plasma exposure of RDV and its metabolites would correlate with or be informative about the exposure of GS-443902 in tissues. A whole body physiologically-based pharmacokinetic (PBPK) modeling and simulation approach was utilized to elucidate the disposition mechanism of RDV and its metabolites in the lungs and liver and explore the relationship between plasma and tissue pharmacokinetics (PK) of RDV and its metabolites in healthy subjects. In addition, the potential alteration of plasma and tissue PK of RDV and its metabolites in patients with organ dysfunction was explored. Our simulation results indicated that intracellular exposure of GS-443902 was decreased in the liver and increased in the lungs in subjects with hepatic impairment relative to the subjects with normal liver function. In subjects with severe renal impairment, the exposure of GS-443902 in the liver was slightly increased, whereas the lung exposure of GS-443902 was not impacted. These predictions along with the organ impairment study results may be used to support decision making regarding the RDV dosage adjustment in these patient subgroups. The modeling exercise illustrated the potential of whole body PBPK modeling to aid in decision making for nucleotide analogue prodrugs, particularly when the active metabolite exposure in the target tissues is not available.

This piece expresses our views on the use of reduction in beta-amyloid (A beta) plaque as a potential tool to facilitate new drug development and patient access to promising therapies for Alzheimer's disease. By summarizing literature from seven anti-A beta antibodies investigated in late-phase trials, we demonstrate a potential threshold of A beta plaque reduction for clinical effect. The reduction in A beta plaque of sufficient extent shows a relationship with clinical improvements as measured by a standard end point.

This paper studies a finite-time adaptive fuzzy control approach for a continuous stirred tank reactor (CSTR) with percent conversion constraint and uncertainties. This system is seen as a class of non-affine systems, and the system is resolved by the mean value theorem. Integral barrier Lyapunov functions (iBLFs) are used to handle output constraint in the design process of the finite-time adaptive controller. In order to calculate the time derivative of the virtual controller, a finite-time convergent differentiator (FTCD) is proposed, which can avert the issue of “explosion of complexity” in the backstepping design. Based on the finite time stability theory, the proposed approach not only ensures the closed-loop stability, but also guarantees tracking performance in a finite time. Finally, the simulation results on CSTR are showed to reveal the availability of the developed control scheme.

The risk in terms of safety or diminished efficacy of switching between an originator biological product and a proposed interchangeable product is an important consideration for interchangeability evaluation in the regulatory framework. This simulation study evaluated the impact of several switching study design scenarios on the pharmacokinetic (PK) assessment between a virtual originator biological product and a virtual proposed interchangeable product. Our results show that (1) at least 3 switches are needed to optimize the detection of potential PK differences, (2) the initial incidence of antidrug antibodies after treatment with the reference product in the lead-in period is a significant covariate affecting the PK results, and (3) the area under the concentration-time curve is more sensitive than peak concentration in assessing the impact of switching on PK similarity. Our simulation work illustrates that a range of factors should be carefully considered when designing a switching study for the assessment of interchangeability between 2 biological products.

On February 24, 2021, the U.S. Food and Drug Administration (FDA) approved an efficacy supplement for HUMIRA® (adalimumab) injection to expand the indication of treatment of moderately to severely active ulcerative colitis (UC) to include pediatric patients 5 years of age and older. The effectiveness in pediatric patients with moderately to severely active UC was studied in a multicenter, randomized, double-blind trial (Study PUC-I, NCT02065557) in 93 pediatric patients 5 to 17 years of age. Adalimumab has been widely studied in multiple indications in adult and pediatric populations with a well-established safety profile; no apparent exposure-safety relationship has been identified in various pediatric populations treated with adalimumab across multiple indications. The approved dosing regimen in pediatric patients with UC differs from the regimen studied in the clinical trial and was determined based on a model-informed exposure bridging strategy, incorporating both efficacy and safety considerations. Specifically, the differences included switches from body weight-based (mg/kg) dosing regimens used in the pediatric trial to body weight-tiered, fixed-dose regimens, changes in dosing schedule, and the addition of an option of a less frequent dosing regimen for maintenance that was not studied in the clinical trial. This article provides a case example of successful model-informed drug development (MIDD), where modeling and simulation were utilized in combination with observed data from a clinical trial of limited size and scope to ultimately support the adalimumab approval in pediatric patients with UC.

Aducanumab was approved in 2021 by the US FDA under the accelerated approval pathway. However, the Office of Clinical Pharmacology at FDA recommended the regular approval based on three key findings. Exposure-response analyses were conducted to quantify the relationship between aducanumab longitudinal exposure and responses (standardized uptake values ratios for beta amyloid and various clinical endpoints) in all clinical trials. To explain the difference between aducanumab and other compounds with negative results in the past, publicly available data were combined with the aducanumab data to demonstrate the relationship between amyloid reduction and clinical endpoint improvement across multiple compounds with similar mechanism of action. The probability to observe the overall positive findings in the aducanumab program was quantified under the assumption that aducanumab is ineffective. Positive exposure-response (disease progression) relationship for multiple clinical endpoints from all clinical trials was identified. Positive exposure-amyloid reduction relationship was established. Consistent amyloid reduction-clinical endpoint improvement relationship across multiple compounds was observed. If aducanumab is assumed to be ineffective, it is extremely impossible to observe the overall positive findings in the aducanumab program. These results provided convincing evidence to support aducanumab’s effectiveness. FDA made the right decision for millions of Alzheimer patients. The observed clinical benefit from aducanumab treatment is clinically meaningful.