Model-based Drug Development

Perioperative durvalumab plus chemotherapy plus new agents for resectable non-small-cell lung cancer: the platform phase II NeoCOAST-2 trial

In the phase II NeoCOAST-2 platform study, 202 patients with untreated, resectable stage IIA-IIIB non-small-cell lung cancer (NSCLC) were randomized to receive neoadjuvant durvalumab plus platinum-doublet chemotherapy with oleclumab, a CD73 inhibitor (Arm 1), or with monalizumab, a NKG2A inhibitor (Arm 2), or neoadjuvant durvalumab plus single-agent platinum chemotherapy with the TROP-2 antibody-drug conjugate (ADC) datopotamab deruxtecan (Arm 4), followed by surgical resection and adjuvant durvalumab with oleclumab or monalizumab (Arms 1 and 2) or durvalumab alone (Arm 4). Primary endpoints were pathological complete response (pCR) rate and safety; secondary endpoints included feasibility of surgery and major pathological response (mPR) rate. In the modified intention-to-treat population (n = 198; Arm 1, n = 74; Arm 2, n = 70; Arm 4, n = 54), pCR rates were 20.3% (15/74; 95% CI, 11.8-31.2), 25.7% (18/70; 95% CI, 16.0-37.6) and 35.2% (19/54; 95% CI, 22.7-49.4), and mPR rates were 41.9% (31/74; 95% CI, 30.5-53.9), 50.0% (35/70; 95% CI, 37.8-62.2) and 63.0% (34/54; 95% CI, 48.7-75.7) in arms 1, 2, and 4, respectively. In the safety population, 69/74 (93.2%), 66/71 (93.0%), and 51/54 (94.4%) patients underwent surgery, respectively. Overall, grade ≥3 treatment-related adverse events occurred in 27/74 (36.5%), 29/71 (40.8%) and 11/54 (20.4%) patients, respectively. In NeoCOAST-2, the first neoadjuvant trial examining an ADC plus chemo-immunotherapy in resectable NSCLC, pCR rates were highest in the datopotamab-deruxtecan-containing arm, warranting further investigation in larger trials of ADCs and checkpoint inhibition in the neoadjuvant setting. ClinicalTrials.gov identifier: NCT05061550 .

The trajectory of pharmacometrics to support drug licensing and labelling

The field of pharmacometrics has been responsible for countless advancements within the drug development space. In recent years, we have witnessed the implementation of both new and revived analytical methods to increase clinical trial success and even supplement the need for clinical trials all together. Throughout this article, we will explore the path of pharmacometrics from its inception to the present day. At this point in time, the target of drug development has been the average patient, and population approaches have primarily been utilized to support just that. The challenge we are now facing involves the translation from treating the typical patient to treating the real-world patient. For this reason, it is our opinion that future development efforts should account more for the individual. With advanced pharmacometric methods and growing technological infrastructure, precision medicine can become a development priority rather than a clinician's burden.

Pre-Posterior Distributions in Drug Development and Their Properties

The topic of this article is pre-posterior distributions of success or failure. These distributions, determined before a study is run and based on all our assumptions, are what we should believe about the treatment effect if we are told only that the study has been successful, or unsuccessful. I show how the pre-posterior distributions of success and failure can be used during the planning phase of a study to investigate whether the study is able to discriminate between effective and ineffective treatments. I show how these distributions are linked to the probability of success (PoS), or failure, and how they can be determined from simulations if standard asymptotic normality assumptions are inappropriate. I show the link to the concept of the conditionalP o S $$ P o S $$ introduced by Temple and Robertson in the context of the planning of multiple studies. Finally, I show that they can also be constructed regardless of whether the analysis of the study is frequentist or fully Bayesian.

The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making

While some model-informed drug development frameworks are well recognized as enabling clinical trials, the value of disease progression modeling (DPM) in impacting medical product development has yet to be fully realized. The Clinical Trials Transformation Initiative assembled a diverse project team from across the patient, academic, regulatory, and industry sectors of practice to advance the use of DPM for decision making in clinical trials and medical product development. This team conducted a scoping review to explore current applications of DPM and convened a multi-stakeholder expert meeting to discuss its value in medical product development. In this article, we present the scoping review and expert meeting output and propose key questions that medical product developers and regulators may use to inform clinical development strategy, appreciate the therapeutic context and endpoint selection, and optimize trial design with disease progression models. By expanding awareness of the unique value of DPM, this article does not aim to be technical in nature but rather aims to highlight the potential of DPM to improve the quality and efficiency of medical product development.

Applications of pharmacometrics in drug development

The last two decades have witnessed profound changes in how advanced computational tools can help leverage tons of data to improve our knowledge, and ultimately reduce cost and increase productivity in drug development. Pharmacometrics has demonstrated its impact through model-informed drug development (MIDD) approaches. It is now an indispensable component throughout the whole continuum of drug discovery, development, regulatory review, and approval. Today, applications of pharmacometrics are common in designing better trials and accelerating evidence-based decisions. Newly emerging technologies, especially those from data and computer sciences, are being integrated with existing computational tools used in the pharmaceutical industry at a remarkably fast pace. The new challenges faced by the pharmacometrics community are not what or how to contribute, but which optimal MIDD strategy should be adopted to maximize its value in the decision-making process. While we are embracing new innovative approaches and tools, this article discusses how a variety of existing modeling tools, with differentiated advantages and focus, can work in concert to inform drug development.

Application of model-informed drug development (MIDD) for dose selection in regulatory submissions for drug approval in Japan

Model-informed drug development (MIDD) is an approach to improve the efficiency of drug development. To promote awareness and application of MIDD in Japan, the Data Science Expert Committee of the Drug Evaluation Committee in the Japan Pharmaceutical Manufacturers Association established a task force, which surveyed MIDD applications for approved products in Japan. This study aimed to reveal the trends and challenges in the use of MIDD by analyzing the survey results. A total of 322 cases approved in Japan between January 2020 and March 2022 as medical products were included in the survey. Modeling analysis was performed in approximately half of the cases (47.8% [154/322]) and formed a major basis for the selection or justification of dosage and administration in approximately one-fourth of the cases [24.2% (78/322)]. Modeling analysis/model-based dose selection was frequently conducted in cases involving monoclonal antibodies, first indication, orphan drugs, and multi-regional trials. Moreover, the survey results indicated that modeling analyses contributed to dose optimization throughout the developmental phases, including changing dose levels from phase II to phase III and dose adjustment in special populations. Japanese data were included in most cases in which modeling analysis was used for dosage selection. Thus, modelling analysis may also address ethnic factors introduced in the ICH E5 and/or E17 guidelines. In summary, this survey is useful for understanding the current status of MIDD use in Japan and for future drug development.

Population Pharmacokinetic and Toxicity Analysis of High-Dose Methotrexate in Patients with Central Nervous System Lymphoma

BACKGROUND

High-dose methotrexate (HD-MTX)-based polychemotherapy is widely used for patients with central nervous system (CNS) lymphoma. The pharmacokinetic (PK) variability and unpredictable occurrence of toxicity remain major concerns in HD-MTX treatment.

OBJECTIVES

This study aimed to characterize the population PK of HD-MTX in patients with CNS lymphoma and to identify baseline predictors and exposure thresholds that predict a high risk of nephro- and hepatotoxicity.

METHODS

Routinely monitored serum MTX concentrations after intravenous infusion of HD-MTX and MTX dosing information were collected retrospectively. Acute event of toxicity (≥ grade 1) was defined according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 on the basis of serum creatinine and alanine aminotransferase. A population PK model was developed in NONMEM. Toxicity data were analyzed using a logistic regression model, and potential baseline and exposure-related predictors were investigated.

RESULTS

In total, 1584 MTX concentrations from 110 patients were available for analysis. A two-compartment population PK model adequately described the data. Estimated glomerular filtration rate (eGFR), treatment regimen, albumin, alkaline phosphatase, and body weight were identified as significant covariates that explain the PK variability of HD-MTX. Baseline eGFR and sex were identified as significant predictors for renal toxicity, and MTX dose (mg/m2) was the strongest predictor for hepatotoxicity. The MTX area under the concentration-time curve (AUC24-∞) and concentration at 24 h (C24h) were shown to correlate with renal toxicity only, and 49,800 μg/L × h (109.6 μmol/L × h) and C24h > 3930 μg/L (8.65 μmol/L) were potential exposure thresholds predicting high risk (proportion > 60%).

CONCLUSIONS

A population PK model was developed for HD-MTX in patients with CNS lymphoma. The toxicity analysis showed that lower baseline eGFR and male sex, and higher MTX dose are associated with increased risk of acute nephro- and hepatotoxicity, respectively. The proposed exposure thresholds that predict high risk of renal toxicity and the developed models hold the potential to guide HD-MTX dosage individualization and better prevent acute toxicity.

Integrated modeling of biomarkers, survival and safety in clinical oncology drug development

Model-based approaches, including population pharmacokinetic-pharmacodynamic modeling, have become an essential component in the clinical phases of oncology drug development. Over the past two decades, models have evolved to describe the temporal dynamics of biomarkers and tumor size, treatment-related adverse events, and their links to survival. Integrated models, defined here as models that incorporate at least two pharmacodynamic/ outcome variables, are applied to answer drug development questions through simulations, e.g., to support the exploration of alternative dosing strategies and study designs in subgroups of patients or other tumor indications. It is expected that these pharmacometric approaches will be expanded as regulatory authorities place further emphasis on early and individualized dosage optimization and inclusive patient-focused development strategies. This review provides an overview of integrated models in the literature, examples of the considerations that need to be made when applying these advanced pharmacometric approaches, and an outlook on the expected further expansion of model-informed drug development of anticancer drugs.

Driving Efficiency: Leveraging Model-Informed Approaches in 505(b)(2) Regulatory Actions

Introduced by the Hatch-Waxman Amendments of 1984, 505(b)(2) applications permit the US Food and Drug Administration to rely, for approval of a new drug application, on information from studies not conducted by or for the applicant and for which the applicant has not obtained a right of reference. This pathway is designed to circumvent the unnecessary duplication of studies already conducted on a previously approved drug. It can lead to a considerably more efficient and expedited route to approval compared to a traditional development path. Model-informed drug development refers to the utilization of a diverse array of quantitative models in drug development to streamline the decision-making process. In this approach, diverse quantitative models that integrate knowledge of physiology, disease processes, and drug pharmacology are employed to address drug development challenges and guide regulatory decisions. Integration of these model-informed approaches into 505(b)(2) regulatory submissions and decision-making can further expedite the approval of new drugs. This article discusses some applications of model-informed approaches that were used to support 505(b)(2) drug development and regulatory actions. Specifically, various quantitative models such as population pharmacokinetic and exposure-response models have been employed to provide evidence of effectiveness, guide dosing in subgroups such as subjects with hepatic or renal impairment, and inform policies. These case study examples collectively underscore the significance of model-informed approaches in drug development and regulatory decisions associated with 505(b)(2) submissions.

Dose, exposure, and treatment regimen of intravenous immunoglobulin G in multifocal motor neuropathy

Introduction

Intravenous immunoglobulin (IVIG) is the only approved treatment for multifocal motor neuropathy (MMN), a rare, chronic, immune-mediated demyelinating neuropathy. There is a significant gap in understanding of the role of serum immunoglobulin G (IgG) levels in the efficacy of IVIG in affected patients. We aimed to characterize the interplay between dose and exposure of IVIG and the effects of patient factors on individual variabilities.

Methods

Serum IgG trough concentration data from a phase 3, randomized, double-blind, placebo-controlled, crossover trial of IVIG 10% in 44 patients with MMN (NCT00666263) were analyzed using fit-for-purpose population PK modeling. Patient factors were tested as covariates, and IgG PK profiles following various dosing regimens were simulated.

Results

Serum IgG levels, with significant inter-patient variability, correlated with dose and treatment interruptions at the individual patient level. Simulated data for various dosing regimens (0.4-2 g/kg once every 1-4 weeks [Q1-4W]) revealed that more frequent dosing provided more stable IgG levels than less frequent dosing, and dose splitting over multiple days had no significant effects on PK.

Discussion

In patients with MMN, stable dosing and consistent serum IgG levels are crucial to avoid negative responses owing to treatment interruptions. Dosing intervals more frequent than Q4W may alleviate periodic symptom deterioration. Dose splitting potentially offers flexibility for patients requiring large volumes of IVIG without negatively affecting serum IgG PK, while maintaining treatment efficacy. Variability in serum IgG levels between patients suggests that individualizing IVIG treatment regimens and target IgG levels may play a key role in managing MMN.

Steady-State Drug Exposure of Repeated IV Bolus Administration for a One Compartment Pharmacokinetic Model with Sigmoidal Hill Elimination

Drugs exhibiting nonlinear pharmacokinetics hold significant importance in drug research and development. However, evaluating drug exposure accurately is challenging with the current formulae established for linear pharmacokinetics. This article aims to investigate the steady-state drug exposure for a one-compartment pharmacokinetic (PK) model with sigmoidal Hill elimination, focusing on three key topics: the comparison between steady-state drug exposure of repeated intravenous (IV) bolus ( AUC ss ) and total drug exposure after a single IV bolus ( AUC 0 - ∞ ); the evolution of steady-state drug concentration with varying dosing frequencies; and the control of drug pharmacokinetics in multiple-dose therapeutic scenarios. For the first topic, we established conditions for the existence of AUC ss , derived an explicit formula for its calculation, and compared it with AUC 0 - ∞ . For the second, we identified the trending properties of steady-state average and trough concentrations concerning dosing frequency. For the third, we developed formulae to compute dose and dosing time for both regular and irregular dosing scenarios. As an example, our findings were applied to a real drug model of progesterone used in lactating dairy cows. In conclusion, these results provide a theoretical foundation for designing rational dosage regimens and conducting therapeutic trials.

Current status and challenges of model-informed drug discovery and development in China

In the past decade, biopharmaceutical research and development in China has been notably boosted by government policies, regulatory initiatives and increasing investments in life sciences. With regulatory agency acting as a strong driver, model-informed drug development (MIDD) is transitioning rapidly from an academic pursuit to a critical component of innovative drug discovery and development within the country. In this article, we provided a cross-sectional summary on the current status of MIDD implementations across early and late-stage drug development in China, illustrated by case examples. We also shared insights into regulatory policy development and decision-making. Various modeling and simulation approaches were presented across a range of applications. Furthermore, the challenges and opportunities of MIDD in China were discussed and compared with other regions where these practices have a more established history. Through this analysis, we highlighted the potential of MIDD to enhance drug development efficiency and effectiveness in China's evolving pharmaceutical landscape.

Use of a PK/PD Model to Select Cetagliptin Dosages for Patients with Type 2 Diabetes in Phase 3 Trials

BACKGROUND

Cetagliptin is a novel dipeptidyl peptidase-4 (DPP-4) inhibitor developed for the treatment of patients with type 2 diabetes (T2D). Several phase 1 studies have been conducted in China. Modelling and simulation were used to obtain cetagliptin dose for phase 3 trials in T2D patients.

METHODS

A pharmacokinetic (PK)/pharmacodynamic (PD) model and model-based analysis of the relationship between hemoglobin A1c (HbA1c) and dosage was explored to guide dose selection of cetagliptin for phase 3 trials. The PK/PD data were derived from four phase 1 clinical studies, and sitagliptin 100 mg was employed as a positive control in studies 1, 3, and 4.

RESULTS

The PK profiles of cetagliptin were well described by a two-compartment model with first-order absorption, saturated efflux, and first-order elimination. The final PD model was a sigmoid maximum inhibitory efficacy (Emax) model with the Hill coefficient. The final model accurately captured cetagliptin PK/PD, demonstrated by goodness-of-fit plots. Based on weighted average inhibition (WAI), the relationship between HbA1c and dose was well displayed. Cetagliptin 50 mg once daily or above as monotherapy or as add-on therapy appeared more effective in HbA1c reduction than sitagliptin 100 mg. Cetagliptin 50 mg or 100 mg once daily was selected as the dose for phase 3 trials of cetagliptin in T2D patients.

CONCLUSIONS

The PK/PD model supports dose selection of cetagliptin for phase 3 trials. A model‑informed approach can be used to replace a dose-finding trial and accelerate cetagliptin's development.

Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity

In recent years, virtual drug study, as an emerging research strategy, has become increasingly important in guiding and promoting new drug research and development. Researchers can integrate a variety of technical methods to improve the efficiency of all phases of new drug research and development, including the use of artificial intelligence, modeling and simulation for target identification, compound screening and pharmacokinetic characteristics evaluation, and the application of clinical trial simulation to carry out clinical research. This paper aims to elaborate on the application of virtual drug study in the key stages of new drug research and development and discuss the opportunities and challenges it faces in supporting new drug research and development.

Model-Informed Assessment of Probability of Phase 3 Success for Ritlecitinib in Patients with Moderate-to-Severe Ulcerative Colitis

Ritlecitinib, an oral Janus kinase 3/tyrosine kinase expressed in hepatocellular carcinoma family inhibitor, was evaluated in patients with ulcerative colitis (UC) in a phase 2b trial. Model-informed drug development strategies were applied to bridge observations from phase 2b to predictions for a proposed phase 3 study design to assess the probability of achieving the target efficacy outcome. A longitudinal exposure-response model of the time course of the 4 Mayo subscores (rectal bleeding, stool frequency, physician's global assessment, and endoscopic subscore) in patients with UC receiving placebo or ritlecitinib was developed using population modeling approaches and an item response theory framework. The quantitative relationships between the 4 Mayo subscores accommodated the prediction of composite endpoints such as total Mayo score and partial Mayo score (key endpoints from phase 2b), and modified clinical remission and endoscopic remission (proposed phase 3 endpoints). Clinical trial simulations using the final model assessed the probability of candidate ritlecitinib dosing regimens (including those tested in phase 2b and alternative) and phase 3 study designs for achieving target efficacy outcomes benchmarked against an approved treatment for moderate-to-severe UC. The probabilities of achieving target modified clinical remission and endoscopic improvement outcomes at both weeks 8 and 52 for ritlecitinib 100 mg once daily was 74.8%. Model-based assessment mitigated some of the risk associated with proceeding to pivotal phase 3 trials with dosing regimens of which there was limited clinical experience.

Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases

The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.

Mechanistic Modeling of In Vitro Biopharmaceutic Data for a Weak Acid Drug: A Pathway Towards Deriving Fundamental Parameters for Physiologically Based Biopharmaceutic Modeling

Mechanistic modeling of in vitro experiments using metabolic enzyme systems enables the extrapolation of metabolic clearance for in vitro-in vivo predictions. This is particularly important for successful clearance predictions using physiologically based pharmacokinetic (PBPK) modeling. The concept of mechanistic modeling can also be extended to biopharmaceutics, where in vitro data is used to predict the in vivo pharmacokinetic profile of the drug. This approach further allows for the identification of parameters that are critical for oral drug absorption in vivo. However, the routine use of this analysis approach has been hindered by the lack of an integrated analysis workflow. The objective of this tutorial is to (1) review processes and parameters contributing to oral drug absorption in increasing levels of complexity, (2) outline a general physiologically based biopharmaceutic modeling workflow for weak acids, and (3) illustrate the outlined concepts via an ibuprofen (i.e., a weak, poorly soluble acid) case example in order to provide practical guidance on how to integrate biopharmaceutic and physiological data to better understand oral drug absorption. In the future, we plan to explore the usefulness of this tutorial/roadmap to inform the development of PBPK models for BCS 2 weak bases, by expanding the stepwise modeling approach to accommodate more intricate scenarios, including the presence of diprotic basic compounds and acidifying agents within the formulation.

Making drugs from T cells: The quantitative pharmacology of engineered T cell therapeutics

Engineered T cells have emerged as highly effective treatments for hematological cancers. Hundreds of clinical programs are underway in efforts to expand the efficacy, safety, and applications of this immuno-therapeutic modality. A primary challenge in developing these "living drugs" is the complexity of their pharmacology, as the drug product proliferates, differentiates, traffics between tissues, and evolves through interactions with patient immune systems. Using publicly available clinical data from Chimeric Antigen Receptor (CAR) T cells, we demonstrate how mathematical models can be used to quantify the relationships between product characteristics, patient physiology, pharmacokinetics and clinical outcomes. As scientists work to develop next-generation cell therapy products, mathematical models will be integral for contextualizing data and facilitating the translation of product designs to clinical strategy.

Three Dimensional Bioprinting for Hepatic Tissue Engineering: From In Vitro Models to Clinical Applications

Fabrication of functional organs is the holy grail of tissue engineering and the possibilities of repairing a partial or complete liver to treat chronic liver disorders are discussed in this review. Liver is the largest gland in the human body and plays a responsible role in majority of metabolic function and processes. Chronic liver disease is one of the leading causes of death globally and the current treatment strategy of organ transplantation holds its own demerits. Hence there is a need to develop an in vitro liver model that mimics the native microenvironment. The developed model should be a reliable to understand the pathogenesis, screen drugs and assist to repair and replace the damaged liver. The three-dimensional bioprinting is a promising technology that recreates in vivo alike in vitro model for transplantation, which is the goal of tissue engineers. The technology has great potential due to its precise control and its ability to homogeneously distribute cells on all layers in a complex structure. This review gives an overview of liver tissue engineering with a special focus on 3D bioprinting and bioinks for liver disease modelling and drug screening.

Incorporation of healthy volunteers data on receptor occupancy into a phase II proof-of-concept trial using a Bayesian dynamic borrowing design

Receptor occupancy in targeted tissues measures the proportion of receptors occupied by a drug at equilibrium and is sometimes used as a surrogate of drug efficacy to inform dose selection in clinical trials. We propose to incorporate data on receptor occupancy from a phase I study in healthy volunteers into a phase II proof-of-concept study in patients, with the objective of using all the available evidence to make informed decisions. A minimal physiologically based pharmacokinetic modeling is used to model receptor occupancy in healthy volunteers and to predict it in the patients of a phase II proof-of-concept study, taking into account the variability of the population parameters and the specific differences arising from the pathological condition compared to healthy volunteers. Then, given an estimated relationship between receptor occupancy and the clinical endpoint, an informative prior distribution is derived for the clinical endpoint in both the treatment and control arms of the phase II study. These distributions are incorporated into a Bayesian dynamic borrowing design to supplement concurrent phase II trial data. A simulation study in immuno-inflammation demonstrates that the proposed design increases the power of the study while maintaining a type I error at acceptable levels for realistic values of the clinical endpoint.

Combining pharmacometric models with predictive and prognostic biomarkers for precision therapy in Crohn's disease: A case study of brazikumab

Pharmacometric models were used to investigate the utility of biomarkers in predicting the efficacy (Crohn's Disease Activity Index [CDAI]) of brazikumab and provide a data-driven framework for precision therapy for Crohn's disease (CD). In a phase IIa trial in patients with moderate to severe CD, treatment with brazikumab, an anti-interleukin 23 monoclonal antibody, was associated with clinical improvement. Brazikumab treatment effect was determined to be dependent on the baseline IL-22 (BIL22) or baseline C-reactive protein (BCRP; predictive biomarkers), and placebo effect was found to be correlated with the baseline CDAI (a prognostic biomarker). A maximal total inhibition on CDAI input function of 50.6% and 42.4% was predicted for patients with extremely high BIL22 or BCRP, compared to a maximal total inhibition of 20.9% and 17.8% for patients with extremely low BIL22 or BCRP, respectively, which were mainly due to the placebo effect. We demonstrated that model-derived baseline biomarker levels that achieve 50% of maximum unbound systemic concentration of 22.8 pg/mL and 8.03 mg/L for BIL22 and BCRP as the cutoffs to select subpopulations can effectively identify high-response subgroup patients with improved separation of responders when compared to using the median values as the cutoff. This work exemplifies the utility of pharmacometrics to quantify biomarker-driven responses in biologic therapies and distinguish between predictive and prognostic biomarkers, complementing clinical efforts of identifying subpopulations with higher likelihood of response to brazikumab.

Efficacy and safety of cetagliptin as monotherapy in patients with type 2 diabetes: A randomized, double-blind, placebo-controlled phase 3 trial

AIM

To assess the efficacy and safety of the dipeptidyl peptidase-4 inhibitor, cetagliptin, as monotherapy in Chinese patients with type 2 diabetes (T2D) and inadequate glycaemic control.

MATERIALS AND METHODS

In total, 504 eligible patients with T2D were enrolled and randomized to cetagliptin 50 mg once daily, cetagliptin 100 mg once daily or placebo at a ratio of 2:2:1 for 24 weeks of double-blind treatment, then all patients received cetagliptin 100 mg once daily for 28 weeks of open-label treatment. The primary efficacy endpoint was the change in HbA1c level from baseline at week 24.

RESULTS

After 24 weeks, HbA1c from baseline was significantly reduced with cetagliptin 50 mg (-1.08%) and cetagliptin 100 mg (-1.07%) compared with placebo (-0.35%). The placebo-subtracted HbA1c reduction was -0.72% with cetagliptin 50 mg and 100 mg. Patients with a baseline HbA1c of 8.5% or higher had a greater HbA1c reduction with cetagliptin than those patients with a baseline HbA1c of less than 8.5%. Both doses studied led to a significantly higher proportion of patients (42.3% with 100 mg and 45.0% with 50 mg) achieving an HbA1c of less than 7.0% compared with placebo (12.9%). Cetagliptin also significantly lowered fasting plasma glucose and 2-hour postmeal plasma glucose relative to placebo. The incidence of adverse experiences was similar between cetagliptin and placebo. No drug-related hypoglycaemia was reported.

CONCLUSIONS

Cetagliptin monotherapy was effective and well tolerated in Chinese patients with T2D who had inadequate glycaemic control on exercise and diet.

Model-informed drug development of autologous CAR-T cell therapy: Strategies to optimize CAR-T cell exposure leveraging cell kinetic/dynamic modeling

Autologous Chimeric antigen receptor (CAR-T) cell therapy has been highly successful in the treatment of aggressive hematological malignancies and is also being evaluated for the treatment of solid tumors as well as other therapeutic areas. A challenge, however, is that up to 60% of patients do not sustain a long-term response. Low CAR-T cell exposure has been suggested as an underlying factor for a poor prognosis. CAR-T cell therapy is a novel therapeutic modality with unique kinetic and dynamic properties. Importantly, "clear" dose-exposure relationships do not seem to exist for any of the currently approved CAR-T cell products. In other words, dose increases have not led to a commensurate increase in the measurable in vivo frequency of transferred CAR-T cells. Therefore, alternative approaches beyond dose titration are needed to optimize CAR-T cell exposure. In this paper, we provide examples of actionable variables - design elements in CAR-T cell discovery, development, and clinical practice, which can be modified to optimize autologous CAR-T cell exposure. Most of these actionable variables can be assessed throughout the various stages of discovery and development as part of a well-informed research and development program. Model-informed drug development approaches can enable such study and program design choices from discovery through to clinical practice and can be an important contributor to cell therapy effectiveness and efficiency.

Modeling breast cancer proliferation, drug synergies, and alternating therapies

Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance, driving the consideration of combination and alternating therapies. Toward this end, we developed a mathematical model that can simulate various mono, combination, and alternating therapies for ER + breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding Cdk4/6 inhibitors to anti-estrogen therapy. Furthermore, the model is used to optimize an alternating treatment protocol so it works as well as monotherapy while using less total drug dose.

Application of Population Balance Model to Simulate Precipitation of Weak Base and Zwitterionic Drugs in Gastrointestinal pH Environment

The purpose of the present study was to evaluate whether the population balance model (PBM) could be a suitable model for the precipitation of weak base and zwitterionic drugs in the gastrointestinal pH environment. Five poorly soluble drugs were used as model drugs (dipyridamole, haloperidol, papaverine, phenazopyridine, and tosufloxacin). PBM consists of the equations for primary nucleation, secondary nucleation, and particle growth. Each equation has two empirical parameters. The pH shift (pH-dumping) precipitation test (pH 3.0 to 6.5) was used to determine the model parameters for each drug. It was difficult to determine all six parameters by simultaneously fitting them to the precipitation profiles. Therefore, the number of model parameters was reduced from six to three by neglecting the secondary nucleation process and applying a common exponent number for the particle growth equation. Despite reducing the parameter number, PBM appropriately described the precipitation profiles in the pH shift tests. The constructed PBM model was then used to predict the precipitation profiles in an artificial stomach-intestine transfer (ASIT) test. PBM appropriately predicted the precipitation profiles in the ASIT test. These results suggested that PBM can be a suitable model to represent the precipitation of weak base and zwitterionic drugs in the gastrointestinal pH environment for biopharmaceutics modeling and simulation.

Bayesian optimal phase II designs with dual-criterion decision making

The conventional phase II trial design paradigm is to make the go/no-go decision based on the hypothesis testing framework. Statistical significance itself alone, however, may not be sufficient to establish that the drug is clinically effective enough to warrant confirmatory phase III trials. We propose the Bayesian optimal phase II trial design with dual-criterion decision making (BOP2-DC), which incorporates both statistical significance and clinical relevance into decision making. Based on the posterior probability that the treatment effect reaches the lower reference value (statistical significance) and the clinically meaningful value (clinical significance), BOP2-DC allows for go/consider/no-go decisions, rather than a binary go/no-go decision. BOP2-DC is highly flexible and accommodates various types of endpoints, including binary, continuous, time-to-event, multiple, and coprimary endpoints, in single-arm and randomized trials. The decision rule of BOP2-DC is optimized to maximize the probability of a go decision when the treatment is effective or minimize the expected sample size when the treatment is futile. Simulation studies show that the BOP2-DC design yields desirable operating characteristics. The software to implement BOP2-DC is freely available at www.trialdesign.org.

Optimizing benefit/risk in oncology: Review of post-marketing dose optimization and reflections on the road ahead

Oncology therapies shifted from chemotherapy to molecularly targeted agents and finally to the era of immune-oncology agents. In contrast to cytotoxic agents, molecularly targeted agents are more selective, exhibit a wider therapeutic window, and may maximally modulate tumor growth at doses lower than the maximum tolerated dose (MTD). However, first-in-patient oncology studies for molecularly targeted agents continued to evaluate escalating doses using limited number of patients per dose cohort assessing dose-limiting toxicities to identify the MTD which is commonly selected for further development adopting a 'more is better' approach that led to several post-marketing requirement (PMR) studies to evaluate alternative, typically lower, doses or dosing frequencies to optimize the benefit-risk profile. In this review, post-marketing dose optimization efforts were reviewed including those required by a regulatory pathway or voluntarily conducted by the sponsor to improve efficacy, safety, or method of administration. Lessons learned and future implications from this deep dive review are discussed considering the evolving regulatory landscape on dose optimization for oncology compounds.

A Bayesian three-tier quantitative decision-making framework for single arm studies in early phase oncology

In early phase oncology drug development, single arm proof-of-concept (POC) studies are increasingly being used to drive the early decisions for future development of the drug. Decision-makings based on such studies, typically involving small sample size and early surrogate efficacy endpoints, are extremely challenging. In particular, given the tremendous competition in the development of immunotherapies, expedition of the most promising programs is desired. To this end, we have proposed a Bayesian three-tier approach to facilitate the decision-making process, inheriting all the benefits of Bayesian decision-making approaches and formally allowing the option of acceleration. With pre-specified Bayesian decision criteria, three types of decisions regarding the future development of the drug can be made: (1) terminating the program, (2) further investigation, considering totality of evidence or additional POC studies, and (3) accelerating the program. We further proposed a Bayesian adaptive three-tier (BAT) design, extending the decision-making approach to incorporate adaptive thresholds and allow for continuous monitoring of the study. We compare the performance of the proposed methods with some other existing methods through simulations.

Mathematically Modeling the Effect of Endocrine and Cdk4/6 Inhibitor Therapies on Breast Cancer Cells

Mathematical modeling of cancer systems is beginning to be used to design better treatment regimens, especially in chemotherapy and radiotherapy. The effectiveness of mathematical modeling to inform treatment decisions and identify therapy protocols, some of which are highly nonintuitive, is because it enables the exploration of a huge number of therapeutic possibilities. Considering the immense cost of laboratory research and clinical trials, these nonintuitive therapy protocols would likely never be found by experimental approaches. While much of the work to date in this area has involved high-level models, which look simply at overall tumor growth or the interaction of resistant and sensitive cell types, mechanistic models that integrate molecular biology and pharmacology can contribute greatly to the discovery of better cancer treatment regimens. These mechanistic models are better able to account for the effect of drug interactions and the dynamics of therapy. The aim of this chapter is to demonstrate the use of ordinary differential equation-based mechanistic models to describe the dynamic interactions between the molecular signaling of breast cancer cells and two key clinical drugs. In particular, we illustrate the procedure for building a model of the response of MCF-7 cells to standard therapies used in the clinic. Such mathematical models can be used to explore the vast number of potential protocols to suggest better treatment approaches.

The Correlated Beta Dose Optimisation Approach: Optimal Vaccine Dosing Using Mathematical Modelling and Adaptive Trial Design

Mathematical modelling methods and adaptive trial design are likely to be effective for optimising vaccine dose but are not yet commonly used. This may be due to uncertainty with regard to the correct choice of parametric model for dose-efficacy or dose-toxicity. Non-parametric models have previously been suggested to be potentially useful in this situation. We propose a novel approach for locating optimal vaccine dose based on the non-parametric Continuous Correlated Beta Process model and adaptive trial design. We call this the 'Correlated Beta' or 'CoBe' dose optimisation approach. We evaluated the CoBe dose optimisation approach compared to other vaccine dose optimisation approaches using a simulation study. Despite using simpler assumptions than other modelling-based methods, we found that the CoBe dose optimisation approach was able to effectively locate the maximum efficacy dose for both single and prime/boost administration vaccines. The CoBe dose optimisation approach was also effective in finding a dose that maximises vaccine efficacy and minimises vaccine-related toxicity. Further, we found that these modelling methods can benefit from the inclusion of expert knowledge, which has been difficult for previous parametric modelling methods. This work further shows that using mathematical modelling and adaptive trial design is likely to be beneficial to locating optimal vaccine dose, ensuring maximum vaccine benefit and disease burden reduction, ultimately saving lives.

Role of Disease Progression Models in Drug Development

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

Improved Confidence in a Confirmatory Stage by Application of Item-Based Pharmacometrics Model: Illustration with a Phase III Active Comparator-Controlled Trial in COPD Patients

PURPOSE

The current study aimed to illustrate how a non-linear mixed effect (NLME) model-based analysis may improve confidence in a Phase III trial through more precise estimates of the drug effect.

METHODS

The FULFIL clinical trial was a Phase III study that compared 24 weeks of once daily inhaled triple therapy with twice daily inhaled dual therapy in patients with chronic obstructive pulmonary disease (COPD). Patient reported outcome data, obtained by using The Evaluating Respiratory Symptoms in COPD (E-RS:COPD) questionnaire, from the FULFIL study were analyzed using an NLME item-based response theory model (IRT). The change from baseline (CFB) in E-RS:COPD total score over 4-week intervals for each treatment arm was obtained using the IRT and compared with published results obtained with a mixed model repeated measures (MMRM) analysis.

RESULTS

The IRT included a graded response model characterizing item parameters and a Weibull function combined with an offset function to describe the COPD symptoms-time course in patients receiving either triple therapy (n = 907) or dual therapy (n = 894). The IRT improved precision of the estimated drug effect compared to MMRM, resulting in a sample size of at least 3.64 times larger for the MMRM analysis to achieve the IRT precision in the CFB estimate.

CONCLUSION

This study shows the advantage of IRT over MMRM with a direct comparison of the same primary endpoint for the two analyses using the same observed clinical trial data, resulting in an increased confidence in Phase III.

Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development

Extrapolation strategies from adult data for designing pediatric drug development programs are explored using the quantitative systems pharmacology (QSP) modeling approach, a mechanistic drug and disease modeling framework that can predict clinical response and guide pediatric drug development in general. This innovative model‐informed drug discovery and development approach can leverage adult‐pediatric pharmacology and disease similarity metrics to validate extrapolation assumptions. We describe the QSP model strategy and framework for extrapolation to design pediatric drug development programs by leveraging adult data across a wide range of therapeutic areas and illustrating stage‐gate decisions informed by such an approach.

Early Feasibility Assessment: A Method for Accurately Predicting Biotherapeutic Dosing to Inform Early Drug Discovery Decisions

The application of model-informed drug discovery and development (MID3) approaches in the early stages of drug discovery can help determine feasibility of drugging a target, prioritize between targets, or define optimal drug properties for a target product profile (TPP). However, applying MID3 in early discovery can be challenging due to the lack of pharmacokinetic (PK) and pharmacodynamic (PD) data at this stage. Early Feasibility Assessment (EFA) is the application of mechanistic PKPD models, built from first principles, and parameterized by data that is readily available early in drug discovery to make effective dose predictions. This manuscript demonstrates the ability of EFA to make accurate predictions of clinical effective doses for nine approved biotherapeutics and outlines the potential of extending this approach to novel therapeutics to impact early drug discovery decisions.

Population pharmacokinetics and IVIVC for mesalazine enteric-coated tablets

Although in-vivo bioequivalence (BE) study serves as a golden standard for establishing interchangeability of oral dosage forms, it remains challenging for products with high inter-subject variability such as mesalazine enteric-coated tablet to fulfil the BE criteria set by regulatory authorities. Mesalazine, as a BCS class IV drug, targets to be delivered to distal ileum or colon with a pH-sensitive polymer coating for the remission of ulcerative colitis. Through population pharmacokinetic (PK) analysis and in-vitro in-vivo correlation (IVIVC) modeling on the dissolution and BE data of a generic enteric-coated product (EM) and its reference Salofalk® 250 mg tablet (SM), we for the first time revealed the underlying mechanism of the high inter-subject variability for such delayed-release formulation. It was also noted that the in-vivo start time of absorption (T_s) for EM and SM was positively correlated with their in-vitro lag time (T_lag) under the USP three-stage dissolution condition and reversely correlated with their in-vivo bioavailability. The varied oral bioavailability of mesalazine enteric-coated tablet was mainly due to the varied N-acetyltransferase activities along GI tract. Although such extensive intestinal first-pass metabolism with large individual differences led to a significant variation of mesalazine C_max (coefficient of variation: 60-63.5%) and AUC_0-t (coefficient of variation: 37.5-46.9%), the corresponding variations in the total absorbed mesalazine (mesalazine and its metabolite N-acetyl mesalazine) were significantly reduced by 12 to 45%. Since the BE purpose for mesalazine enteric-coated tablet focused on their comparable safety profiles, total absorbed mesalazine was recommended to be adopted for the development of the IVIVC model and BE evaluation for EM. All in all, our model-based approach has not only successfully identified the key factors that affect the BE of EM to guide its further formulation optimization, but also demonstrated the indispensable role of modeling in the development of generic pharmaceutical product at its early stages.

Achieving end-to-end success in the clinic: Pfizer's learnings on R&D productivity

Over the past decade, Pfizer has focused efforts to improve its research and development (R&D) productivity. By the end of 2020, Pfizer had achieved an industry-leading clinical success rate of 21%, a tenfold increase from 2% in 2010 and well above the industry benchmark of ∼11%. The company had also maintained the quality of innovation, because 75% of its approvals between 2016 and 2020 had at least one expedited regulatory designation (e.g., Breakthrough Therapy). Pfizer's Signs of Clinical Activity (SOCA) paradigm enabled better decision-making and, along with other drivers (biology and modality), contributed to this productivity improvement. These laid a strong foundation for the rapid and effective development of the Coronavirus 2019 (COVID-19) vaccine with BioNTech, as well as the antiviral candidate Paxlovid™, under the company's 'lightspeed' paradigm.

Comparing Go/No-Go Decision-Making Properties Between Single Arm Phase II Trial Designs in Oncology

INTRODUCTION

Simon's design has been widely used in oncology to conduct single arm phase II trials and to make Go/No-Go development decision. Other authors have proposed designs with decision-making frameworks that include a third, "Consider" outcome. For results in the Consider zone, a final Go/No-Go development decision must still be made; however it is typically a subjective decision based on the totality of data and the development landscape. Under this framework, the probability of continuing development when the candidate therapy is truly ineffective or the probability of stopping development when the candidate therapy is truly effective is undefined.

METHODS

We use a motivating example to compare end of trial decision-making between Simon's two-stage approach and a Multilevel outcome approach. We present the minimum and maximum development decision error probabilities by varying whether candidates that end in the Consider zone would ultimately continue with development or not.

RESULTS

The Multilevel approach typically requires fewer patients, but the risk of making an incorrect drug development decision is inflated above the statistically defined Type I and Type II error rates. Compared to a Type I error rate of 20%, the Multilevel trial's maximum probability of moving forward with an ineffective therapy is 22%, 27%, and 36% for Consider zone sizes of 10%, 20%, and 30%, respectively.

CONCLUSION

The Multilevel approach provides flexibility in interpreting moderate efficacy results. However, the flexibility is accomplished with a lower sample size and corresponding uncertainty in the trial outcome that increases the risk of incorrect drug development decisions.

Value of Literature Review to Inform Development and Use of Biologics in Juvenile Idiopathic Arthritis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is one of the most common pediatric inflammatory rheumatic diseases (PiRDs). Uncontrolled disease activity is associated with decreased quality of life and chronic morbidity. Biologic disease-modifying antirheumatic drugs (bDMARDs) and Janus kinase inhibitors (JAKi) have considerably improved clinical outcomes. For optimized patient care, understanding the efficacy-safety profile of biologics in subgroups of JIA is crucial. This systematic review based on published randomized controlled trials (RCTs) aims to assess efficacy and safety data for bDMARDs and JAKi with various JIA subgroups after 3 months of treatment.

METHODS

Data for American College of Rheumatology (ACR) pediatric (Pedi) 30, 50, and/or 70 responses after 3 months of treatment were selected from RCTs investigating bDMARDs or JAKi in JIA according to predefined inclusion/exclusion criteria. Treatment and control arms were compared by calculating risk ratios (RRs) with 95% confidence intervals (CIs), and proportions of overall, serious adverse events (AEs) and infections were analyzed. Forest plots were generated to summarize efficacy and safety endpoints across studies, JIA subgroups, and type of biologics.

RESULTS

Twenty-eight out of 41 PiRD RCTs investigated bDMARD or JAKi treatments in JIA. 9 parallel RCTs reported ACR Pedi 30, 50, and/or 70 responses 3 months after treatment initiation. All treatment arms showed improved ACR Pedi responses over controls. RRs ranged from 1.05 to 3.73 in ACR Pedi 30, from 1.20 to 7.90 in ACR Pedi 50, and from 1.19 to 8.73 in ACR Pedi 70. An enhanced effect for ACR Pedi 70 was observed with infliximab combined with methotrexate in PJIA vs. methotrexate monotherapy. A slightly higher risk of gastrointestinal AEs and infections was observed with treatment arms compared to placebo or methotrexate monotherapy.

CONCLUSION

Investigated bDMARDs and JAKi showed superior treatment responses compared to controls after 3 months of treatment, which were more pronounced in ACR Pedi 50 and 70 than in ACR Pedi 30. Higher susceptibility to infections associated with bDMARDs or JAKi vs. control arms must be weighed against efficacious treatment of the underlying disease and prevention of disease-related damage. Additional RCTs are warranted to further inform development and utilization of biologics in JIA.

Computational Methods for Structure-Based Drug Design Through System Biology

The advances in computational chemistry and biology, computer science, structural biology, and molecular biology go in parallel with the rapid progress in target-based systems. This technique has become a powerful tool in medicinal chemistry for the identification of hit molecules. The recent developments in target-based systems have played a major role in the creation of libraries of compounds, and it has also been widely applied for the design of molecular docking methods. The main advantage of this method is that it hits the fragment that has the strongest binding, has relatively small size, and leads to better compounds in terms of pharmacokinetic properties when compared with virtual screening (VS) and high-throughput screening (HTS) hits. De novo design is an essential aspect of target-based systems and requires the synthesis of chemical to allow the design of promising compound.

Combined Methodologies for Determining In Vitro Bioavailability of Drugs and Prediction of In Vivo Bioequivalence From Pharmaceutical Oral Formulations

With the aim of developing an in vitro model for the bioavailability (BA) prediction of drugs, we focused on the study of levonorgestrel (LVN) released by 1.5 mg generic and brand-name tablets. The developed method consisted in combining a standard dissolution test with an optimized parallel artificial membrane permeability assay (PAMPA) to gain insights into both drug release and gastrointestinal absorption. Interestingly, the obtained results revealed that the tablet standard dissolution test, combined with an optimized PAMPA, highlighted a significant decrease in the release (15 ± 0.01 μg min⁻¹ vs 30 ± 0.01 μg min⁻¹) and absorption (19 ± 7 × 10^–6 ± 7 cm/s Pe vs 41 ± 15 × 10^–6 cm/s Pe) profiles of a generic LVN tablet when compared to the brand-name formulation, explaining unbalanced in vivo bioequivalence (BE). By using this new approach, we could determine the actual LVN drug concentration dissolved in the medium, which theoretically can permeate the gastrointestinal (GI) barrier. In fact, insoluble LVN/excipient aggregates were found in the dissolution media giving rise to non-superimposable dissolution profiles between generic and brand-name LVN tablets. Hence, the results obtained by combining the dissolution test and PAMPA method provided important insights confirming that the combined methods can be useful in revealing crucial issues in the prediction of in vivo BE of drugs.

A continued learning approach for model-informed precision dosing: updating models in clinical practice

Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to include also altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, since only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step towards building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.

A New Comprehensive Approach to Assess the Probability of Success of Development Programs Before Pivotal Trials

The point at which clinical development programs transition from early phase to pivotal trials is a critical milestone. Substantial uncertainty about the outcome of pivotal trials may remain even after seeing positive early phase data, and companies may need to make difficult prioritization decisions for their portfolio. The probability of success (PoS) of a program, a single number expressed as a percentage reflecting the multitude of risks that may influence the final program outcome, is a key decision-making tool. Despite its importance, companies often rely on crude industry benchmarks that may be "adjusted" by experts based on undocumented criteria and which are typically misaligned with the definition of success used to drive commercial forecasts, leading to overly optimistic expected net present value calculations. We developed a new framework to assess the PoS of a program before pivotal trials begin. Our definition of success encompasses the successful outcome of pivotal trials, regulatory approval and meeting the requirements for market access as outlined in the target product profile. The proposed approach is organized in four steps and uses an innovative Bayesian approach to synthesize all relevant evidence. The new PoS framework is systematic and transparent. It will help organizations to make more informed decisions. In this paper, we outline the rationale and elaborate on the structure of the proposed framework, provide examples, and discuss the benefits and challenges associated with its adoption.

Model-Based Approach for Designing an Efficient Bioequivalence Study for Highly Variable Drugs

The statistical procedures as outlined by the European Medicines Agency (EMA) and United States Food and Drug Administration (FDA) guidelines for bioequivalence testing of highly variable drugs (HVDs) are complex. Additionally, the sample size is affected by clinical study designs or practical real-world problems, such as dropout rate or study budget. To overcome these difficulties, we propose a model-based approach for the selection of a study design with a sample size that satisfies the bioequivalence criteria using simulation studies based on a pharmacokinetic (PK) model. The designed approach was implemented using a simulation procedure considering some conventionally measured factors, such as geometric mean ratio and within-subject coefficient of variation, with various PK information important in determining bioequivalence. All simulation results were assessed according to the EMA and FDA guidelines. Furthermore, power calculations from simulation results were interpreted with regard to PK characteristics and compared among 2 × 2, 3 × 3, and 2 × 4 crossover designs to determine the efficient design considering appropriate sample size and duration of the clinical study. The proposed approach can be applied to bioequivalence studies of all drugs. However, the current study was targeted at HVDs, which are highly likely to require detailed decision making for sample size and study design.