Model-based drug development: a rational approach to efficiently accelerate drug development

Bioengineered Probiotics for Clostridioides difficile Infection: An Overview of the Challenges and Potential for This New Treatment Approach

The rapid increase in microbial antibiotic resistance in Clostridioides difficile (C. difficile) strains and the formation of hypervirulent strains have been associated with a global increase in the incidence of C. difficile infection (CDI) and subsequently, an increase in the rate of recurrence. These consequences have led to an urgent need to develop new and promising alternative strategies to control this pathogen. Engineered probiotics are exciting new bacterial strains produced by editing the genome of the original probiotics. Recently, engineered probiotics have been used to develop delivery vehicles for vaccines, diagnostics, and therapeutics. Recent studies have demonstrated engineered probiotics may potentially be an effective approach to control or treat CDI. This review provides a brief overview of the considerations for engineered probiotics for medicinal use, with a focus on recent preclinical research using engineered probiotics to prevent or treat CDI. We also address the challenges faced in the production of engineered strains and how they may be overcome in the application of these agents to meet patient needs in the future.

Exposure-Response Analysis of Tofacitinib in Active Psoriatic Arthritis: Results from Two Phase 3 Studies

Tofacitinib is an oral Janus kinase inhibitor for the treatment of psoriatic arthritis (PsA). These post hoc exposure-response (E-R) analyses of pooled data from two Phase 3 studies (NCT01877668 and NCT01882439) characterized the relationships between tofacitinib exposure and efficacy (American College of Rheumatology [ACR] criteria), and changes in hemoglobin (Hgb) in patients with PsA. Efficacy data for the proportion of patients receiving tofacitinib 5 or 10 mg twice daily, or placebo, achieving ACR ≥20%, ≥50%, or ≥70% response criteria (ACR20, ACR50, and ACR70, respectively) at Month 3, were modeled jointly using a four-category ordered categorical exposure-response model (ACR20 non-responder, ACR20 responder but not ACR50 responder, ACR50 responder but not ACR70 responder, and ACR70 responder). A maximum drug effect (Emax) model (using average concentrations of tofacitinib at steady state [Cavg]) adequately described the exposure-ACR response rate relationship. Model-predicted response rates for tofacitinib 5 and 10 mg twice daily were 51% and 58%, respectively, for ACR20; 29% and 36% for ACR50; and 15% and 20% for ACR70. The E-R relationship between tofacitinib exposure and changes in Hgb was assessed using an indirect response model, which generally predicted Hgb concentration-time profiles across treatments well. The proportions of patients experiencing a decrease in Hgb of >2 g/dL were similar with tofacitinib 5 mg twice daily or placebo. These results were generally consistent with previous analyses in rheumatoid arthritis and psoriasis, and support the use of tofacitinib 5 mg twice daily for active PsA.

Dynamic Modeling of Cell Cycle Arrest Through Integrated Single-Cell and Mathematical Modelling Approaches

Highly multiplexed imaging assays allow simultaneous quantification of multiple protein and phosphorylation markers, providing a static snapshots of cell types and states. Pseudo-time techniques can transform these static snapshots of unsynchronized cells into dynamic trajectories, enabling the study of dynamic processes such as development trajectories and the cell cycle. Such ordering also enables training of mathematical models on these data, but technical challenges have hitherto made it difficult to integrate multiple experimental conditions, limiting the predictive power and insights these models can generate. In this work, we propose data processing and model training approaches for integrating multiplexed, multi-condition immunofluorescence data with mathematical modelling. We devise training strategies that are applicable to datasets where cells exhibit oscillatory as well as arrested dynamics and use them to train a cell cycle model on a dataset of MCF-10A mammary epithelial exposed to cell-cycle arresting small molecules. We validate the model by investigating predicted growth factor sensitivities and responses to inhibitors of cells at different initial conditions. We anticipate that our framework will generalise to other highly multiplexed measurement techniques such as mass-cytometry, rendering larger bodies of data accessible to dynamic modelling and paving the way to deeper biological insights.

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.

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.

Application of Quantitative Systems Pharmacology Approaches to Support Pediatric Labeling in Rare Diseases

Quantitative Systems Pharmacology (QSP) models offer a promising approach to extrapolate drug efficacy across different patient populations, particularly in rare diseases. Unlike conventional empirical models, QSP models can provide a mechanistic understanding of disease progression and therapeutic response by incorporating current disease knowledge into the descriptions of biomarkers and clinical endpoints. This allows for a holistic representation of the disease and drug response. The mechanistic nature of QSP models is well suited to pediatric extrapolation concepts, providing a quantitative method to assess disease and drug response similarity between adults and pediatric patients. The application of a QSP-based assessment of the disease and drug similarity in adult and pediatric patients in the clinical development program of olipudase alfa, a treatment for Acid Sphingomyelinase Deficiency (ASMD), illustrates the potential of this approach.

Quantitative Systems Pharmacology Models: Potential Tools for Advancing Drug Development for Rare Diseases

Rare diseases, affecting millions globally, present significant drug development challenges. This is due to the limited patient populations and the unique pathophysiology of these diseases, which can make traditional clinical trial designs unfeasible. Quantitative Systems Pharmacology (QSP) models offer a promising approach to expedite drug development, particularly in rare diseases. QSP models provide a mechanistic representation of the disease and drug response in virtual patients that can complement routinely applied empirical modeling and simulation approaches. QSP models can generate digital twins of actual patients and mechanistically simulate the disease progression of rare diseases, accounting for phenotypic heterogeneity. QSP models can also support drug development in various drug modalities, such as gene therapy. Impactful QSP models case studies are presented here to illustrate their value in supporting various aspects of drug development in rare indications. As these QSP model applications continue to mature, there is a growing possibility that they could be more widely integrated into routine drug development steps. This integration could provide a robust framework for addressing some of the inherent challenges in rare disease drug development.

Model-informed drug discovery and development approaches to inform clinical trial design and regulatory decisions: A primer for the MENA region

Model-Informed Drug Discovery and Development (MID3) represents a transformative approach in pharmaceutical research, integrating quantitative models to inform and optimize decision-making throughout the drug development process. This review explores the current applications, challenges, and future prospects of MID3 within the Middle East and North Africa (MENA) region. By leveraging local data and advanced computational techniques, MID3 has the potential to significantly enhance the efficiency and success rates of drug development tailored to regional health priorities. We discussed successful case studies of applying MID3 at different phases of drug development and clinical trials. Furthermore, we emphasized the critical need for MENA countries to embrace MID3 by investing in workforce training, aligning regulatory frameworks, and fostering collaborative research initiatives. This call to action underscores the importance of a robust MID3 ecosystem, urging policymakers, academic institutions, and industry stakeholders to prioritize and support its integration into the MENA region's healthcare.

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.

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.

A fully automatic tool for development of population pharmacokinetic models

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.

Simulation-based evaluation of the Pharmpy Automatic Model Development tool for population pharmacokinetic modeling in early clinical drug development

The Pharmpy Automatic Model Development (AMD) tool automates the building of population pharmacokinetic (popPK) models by utilizing a systematic stepwise process. In this study, the performance of the AMD tool was assessed using simulated datasets. Ten true models mimicking classical popPK models were created. From each true model, dataset replicates were simulated assuming a typical phase I study design-single and multiple ascending doses with/without dichotomous food effect, with rich PK sampling. For every dataset replicate, the AMD tool automatically built an AMD model utilizing NONMEM for parameter estimation. The AMD models were compared to the true and reference models (true model fitted to simulated datasets) based on their model components, predicted population and individual secondary PK parameters (SP) (AUC0-24, cmax, ctrough), and model quality metrics (e.g., model convergence, parameter relative standard errors (RSEs), Bayesian Information Criterion (BIC)). The models selected by the AMD tool closely resembled the true models, particularly in terms of distribution and elimination, although differences were observed in absorption and inter-individual variability components. Bias associated with the derived SP was low. In general, discrepancies between AMD and true SP were also observed for reference models and therefore were attributed to the inherent stochasticity in simulations. In summary, the AMD tool was found to be a valuable asset in automating repetitive modeling tasks, yielding reliable PK models in the scenarios assessed. This tool has the potential to save time during early clinical drug development that can be invested in more complex modeling activities within model-informed drug development.

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.

Method "Monte Carlo" in healthcare

In public health, simulation modeling stands as an invaluable asset, enabling the evaluation of new systems without their physical implementation, experimentation with existing systems without operational adjustments, and testing system limits without real-world repercussions. In simulation modeling, the Monte Carlo method emerges as a powerful yet underutilized tool. Although the Monte Carlo method has not yet gained widespread prominence in healthcare, its technological capabilities hold promise for substantial cost reduction and risk mitigation. In this review article, we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts. We underscore the significance of experiential insights derived from simulated experimentation, especially in resource-constrained scenarios where time, financial constraints, and limited resources necessitate innovative and efficient approaches. As public health faces increasing challenges, incorporating the Monte Carlo method presents an opportunity for enhanced system construction, analysis, and evaluation.

From Plan to Pivot: How Model-Informed Drug Development Shaped the Dose Strategy of the Zibotentan/Dapagliflozin ZENITH Trials

Getting the dose right is a key challenge in drug development; model-informed drug development (MIDD) provides powerful tools to shape dose strategies and inform decision making. In this tutorial, the case study of the ZENITH trials showcases how a set of clinical pharmacology and MIDD approaches informed an impactful dose strategy. The endothelin A receptor antagonist zibotentan, combined with the sodium-glucose co-transporter-2 inhibitor dapagliflozin, has yielded a robust and significant albuminuria reduction in the Phase IIb trial ZENITH-CKD and is being investigated for reduction of kidney function decline in a high-risk chronic kidney disease population in the Phase III trial ZENITH High Proteinuria. Endothelin antagonist treatment has, until now, been limited by the class effect fluid retention. ZENITH-CKD investigated a wide range of zibotentan doses based on pharmacokinetics in renal impairment, competitor-data exposure-response modeling, and clinical trial simulations. Recruitment delays reduced interim analysis data availability; here, supportive dose-response modeling recovered decision-making confidence. At trial completion, the low-dose arm enabled Phase III dose selection between Phase IIb doses. Dose-response modeling of efficacy and Kaplan-Meier analyses of tolerability identified a kidney-function-based low-dose strategy of 0.25 or 0.75 mg zibotentan (with 10 mg dapagliflozin) to balance benefit/risk in ZENITH High Proteinuria. The applied clinical pharmacology and MIDD principles enabled successful Phase IIb dose finding, rationalized and built confidence in the innovative Phase III dosing strategy and identified a potential therapeutic window for zibotentan/dapagliflozin, providing the opportunity for a significant improvement in the treatment of chronic kidney disease with high proteinuria.

Predicting the Long-Term Effects of Therapeutic Neutralization of Oncostatin M on Human Hematopoiesis

Therapeutic neutralization of Oncostatin M (OSM) causes mechanism-driven anemia and thrombocytopenia, which narrows the therapeutic window complicating the selection of doses (and dosing intervals) that optimize efficacy and safety. We utilized clinical data from studies of an anti-OSM monoclonal antibody (GSK2330811) in healthy volunteers (n = 49) and systemic sclerosis patients (n = 35), to quantitatively determine the link between OSM and alterations in red blood cell (RBC) and platelet production. Longitudinal changes in hematopoietic variables (including RBCs, reticulocytes, platelets, erythropoietin, and thrombopoietin) were linked in a physiology-based model, to capture the long-term effects and variability of therapeutic OSM neutralization on human hematopoiesis. Free serum OSM stimulated precursor cell production through sigmoidal relations, with higher maximum suppression (Imax) and OSM concentration for 50% suppression (IC50) for platelets (89.1% [95% confidence interval: 83.4-93.0], 6.03 pg/mL [4.41-8.26]) than RBCs (57.0% [49.7-64.0], 2.93 pg/mL [2.55-3.36]). Reduction in hemoglobin and platelets increased erythro- and thrombopoietin, respectively, prompting reticulocytosis and (partially) alleviating OSM-restricted hematopoiesis. The physiology-based model was substantiated by preclinical data and utilized in exploration of once-weekly or every other week dosing regimens. Predictions revealed an (for the indication) unacceptable occurrence of grade 2 (67% [58-76], 29% [20-38]) and grade 3 (17% [10-25], 3% [0-7]) anemias, with limited thrombocytopenia. Individual extent of RBC precursor modulation was moderately correlated to skin mRNA gene expression changes. The physiological basis and consideration of interplay among hematopoietic variables makes the model generalizable to other drug and nondrug scenarios, with adaptations for patient populations, diseases, and therapeutics that modulate hematopoiesis or exhibit risk of anemia and/or thrombocytopenia.

Pharmacokinetic-Pharmacodynamic Correlation Analysis of Rhodiola crenulata in Rats with Myocardial Ischemia

The pharmacokinetics (PK) of Rhodiola crenulata in rats were studied, and pharmacokinetic-pharmacodynamic (PK-PD) correlation analysis was performed to elucidate their time-concentration-effect relationship. The myocardial ischemia model was made with pituitrin. Rats were divided into sham operation, sham operation administration, model, and model administration groups (SG, SDG, MG, and MDG, respectively; n = 6). Blood was collected from the fundus venous plexus at different time points after oral administration. The HPLC-QQQ-MS/MS method was established for the quantification of five components of Rhodiola crenulata. CK, HBDH, SOD, LDH, and AST at different time points were detected via an automatic biochemical analyzer. DAS software was used to analyze PK parameters and PK-PD correlation. The myocardial ischemia model was established successfully. There were significant differences in the PK parameters (AUC0-t, AUC0-∞, Cmax) in MDG when compared with SDG. Two PD indicators, CK and HBDH, conforming to the sigmoid-Emax model, had high correlation with the five components, which indicated a delay in the pharmacological effect relative to the drug concentration in plasma. The difference in the PK parameters between modeled and normal rats was studied, and the time-concentration-effect of composition and effect indicators were investigated. This study can provide reference for the rational clinical application of Rhodiola crenulata and for related studies of other anti-myocardial ischemia drugs.

Accelerating therapeutics development during a pandemic: population pharmacokinetics of the long-acting antibody combination AZD7442 (tixagevimab/cilgavimab) in the prophylaxis and treatment of COVID-19

AZD7442 is a combination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-neutralizing antibodies, tixagevimab and cilgavimab, developed for pre-exposure prophylaxis (PrEP) and treatment of coronavirus disease 2019 (COVID-19). Using data from eight clinical trials, we describe a population pharmacokinetic (popPK) model of AZD7442 and show how modeling of "interim" data accelerated decision-making during the COVID-19 pandemic. The final model was a two-compartmental distribution model with first-order absorption and elimination, including standard allometric exponents for the effect of body weight on clearance and volume. Other covariates included were as follows: sex, age >65 years, body mass index ≥30 kg/m2, and diabetes on absorption rate; diabetes on clearance; Black race on central volume; and intramuscular (IM) injection site on bioavailability. Simulations indicated that IM injection site and body weight had > 20% effects on AZD7442 exposure, but no covariates were considered to have a clinically relevant impact requiring dose adjustment. The pharmacokinetics of AZD7442, cilgavimab, and tixagevimab were comparable and followed linear kinetics with extended half-lives (median 78.6 days for AZD7442), affording prolonged protection against susceptible SARS-CoV-2 variants. Comparison of popPK simulations based on "interim data" with a target concentration based on 80% viral inhibition and assuming 1.81% partitioning into the nasal lining fluid supported a decision to double the PrEP dosage from 300 mg to 600 mg to prolong protection against Omicron variants. Serum AZD7442 concentrations in adolescents weighing 40-95 kg were predicted to be only marginally different from those observed in adults, supporting authorization for use in adolescents before clinical data were available. In these cases, popPK modeling enabled accelerated clinical decision-making.

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.

Network meta analysis to predict the efficacy of an approved treatment in a new indication

Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not widely used in practice. Instead, repurposing decisions are often based on subjective judgments from limited empirical evidence. In this article, we develop a novel Bayesian network meta-analysis (NMA) framework that can predict the efficacy of an approved treatment in a new indication and thereby identify candidate treatments for repurposing. We obtain predictions using two main steps: first, we use standard NMA modeling to estimate average relative effects from a network comprised of treatments studied in both indications in addition to one treatment studied in only one indication. Then, we model the correlation between relative effects using various strategies that differ in how they model treatments across indications and within the same drug class. We evaluate the predictive performance of each model using a simulation study and find that the model minimizing root mean squared error of the posterior median for the candidate treatment depends on the amount of available data, the level of correlation between indications, and whether treatment effects differ, on average, by drug class. We conclude by discussing an illustrative example in psoriasis and psoriatic arthritis and find that the candidate treatment has a high probability of success in a future trial.

In Silico Clinical Trials: Is It Possible?

Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

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.

Quantitative Systems Pharmacology and Machine Learning: A Match Made in Heaven or Hell?

As pharmaceutical development moves from early-stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realizing the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modeling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data are highlighted in informing the initial use of ML or mechanistic quantitative systems pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. SIGNIFICANCE STATEMENT: We consider when best to apply machine learning (ML) and mechanistic quantitative systems pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.

Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma

BACKGROUND

Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported.

RESULTS

We highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use.

CONCLUSIONS

With this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy.

Safety and pharmacokinetics of IBI112, an IL-23 monoclonal antibody, in Chinese healthy volunteers: a first-in-human phase 1 study

BACKGROUND

Interleukin (IL) 23p19 monoclonal antibodies were efficacious and safe in the treatment of psoriasis. A first-in-human (FIH) study was conducted to evaluate the safety, tolerability, pharmacokinetics (PK) and immunogenicity of IBI112, a novel IL-23p19 monoclonal antibody.

METHODS

In this FIH, randomized, double-blind, placebo-controlled, single-ascending-dose study, a subcutaneous (SC, 5-600 mg) or intravenous (IV, 100 and 600 mg) or placebo was administered to eligible healthy subjects. Safety was assessed by physical examinations, vital signs, laboratory tests, and electrocardiograms. Furthermore, non-compartment analysis and population PK modeling were conducted to characterize PK, and model-based simulation was applied to justify dose selection for psoriasis patients.

RESULTS

A total of 46 subjects were enrolled, with 35 receiving IBI112 and 11 receiving placebo. No serious adverse events (SAEs) and no clinically significant adverse events were identified. After a single SC of IBI112, the median Tmax was 4-10.5 days, and the half-life (t1/2) ranged from 21.8 to 35.8 days. IBI112 exposures (Cmax and AUCinf) approached dose proportionality across 5-300 mg range.

CONCLUSION

IBI112 was well tolerated and safe at SC or IV dose up to 600 mg and showed a linear PK characteristics at SC dose from 5 to 300 mg.

CLINICAL TRIAL REGISTRATION

ClinicalTrial.gov NCT04511624.

Dose Optimization in Oncology Drug Development: The Emerging Role of Pharmacogenomics, Pharmacokinetics, and Pharmacodynamics

Drugs' safety and effectiveness are evaluated in randomized, dose-ranging trials in most therapeutic areas. However, this is only sometimes feasible in oncology, and dose-ranging studies are mainly limited to Phase 1 clinical trials. Moreover, although new treatment modalities (e.g., small molecule targeted therapies, biologics, and antibody-drug conjugates) present different characteristics compared to cytotoxic agents (e.g., target saturation limits, wider therapeutic index, fewer off-target side effects), in most cases, the design of Phase 1 studies and the dose selection is still based on the Maximum Tolerated Dose (MTD) approach used for the development of cytotoxic agents. Therefore, the dose was not optimized in some cases and was modified post-marketing (e.g., ceritinib, dasatinib, niraparib, ponatinib, cabazitaxel, and gemtuzumab-ozogamicin). The FDA recognized the drawbacks of this approach and, in 2021, launched Project Optimus, which provides the framework and guidance for dose optimization during the clinical development stages of anticancer agents. Since dose optimization is crucial in clinical development, especially of targeted therapies, it is necessary to identify the role of pharmacological tools such as pharmacogenomics, therapeutic drug monitoring, and pharmacodynamics, which could be integrated into all phases of drug development and support dose optimization, as well as the chances of positive clinical outcomes.

Identifying COVID-19 optimal vaccine dose using mathematical immunostimulation/immunodynamic modelling

INTRODUCTION

Identifying optimal COVID-19 vaccine dose is essential for maximizing their impact. However, COVID-19 vaccine dose-finding has been an empirical process, limited by short development timeframes, and therefore potentially not thoroughly investigated. Mathematical IS/ID modelling is a novel method for predicting optimal vaccine dose which could inform future COVID-19 vaccine dose decision making.

METHODS

Published clinical data on COVID-19 vaccine dose-response was identified and extracted. Mathematical models were calibrated to the dose-response data stratified by subpopulation, where possible to predict optimal dose. Predicted optimal doses were summarised across vaccine type and compared to chosen dose for the primary series of COVID-19 vaccines to identify vaccine doses that may benefit from re-evaluation.

RESULTS

30 clinical dose-response datasets in adults and elderly population were extracted for four vaccine types and optimal doses predicted using the models. Results suggest that, if re-assessed for dose, COVID-19 vaccines Ad26.cov, ChadOx1 n-Cov19, BNT162b2, Coronavac, and NVX-CoV2373 could benefit from increased dose in adults and mRNA-1273 and Coronavac, could benefit from increased and decreased dose for the elderly population, respectively.

DISCUSSION

Future iterations of COVID-19 vaccines could benefit from re-evaluating dose to ensure most effective use of the vaccine and mathematical modelling can support this.

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.

Social pharmaceutical innovation and alternative forms of research, development and deployment for drugs for rare diseases

Rare diseases are associated with difficulties in addressing unmet medical needs, lack of access to treatment, high prices, evidentiary mismatch, equity, etc. While challenges facing the development of drugs for rare diseases are experienced differently globally (i.e., higher vs. lower and middle income countries), many are also expressed transnationally, which suggests systemic issues. Pharmaceutical innovation is highly regulated and institutionalized, leading to firmly established innovation pathways. While deviating from these innovation pathways is difficult, we take the position that doing so is of critical importance. The reason is that the current model of pharmaceutical innovation alone will not deliver the quantity of products needed to address the unmet needs faced by rare disease patients, nor at a price point that is sustainable for healthcare systems. In light of the problems in rare diseases, we hold that re-thinking innovation is crucial and more room should be provided for alternative innovation pathways. We already observe a significant number and variety of new types of initiatives in the rare diseases field that propose or use alternative pharmaceutical innovation pathways which have in common that they involve a diverse set of societal stakeholders, explicitly address a higher societal goal, or both. Our position is that principles of social innovation can be drawn on in the framing and articulation of such alternative pathways, which we term here social pharmaceutical innovation (SPIN), and that it should be given more room for development. As an interdisciplinary research team in the social sciences, public health and law, the cases of SPIN we investigate are spread transnationally, and include higher income as well as middle income countries. We do this to develop a better understanding of the social pharmaceutical innovation field's breadth and to advance changes ranging from the bedside to system levels. We seek collaborations with those working in such projects (e.g., patients and patient organisations, researchers in rare diseases, industry, and policy makers). We aim to add comparative and evaluative value to social pharmaceutical innovation, and we seek to ignite further interest in these initiatives, thereby actively contributing to them as a part of our work.

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.

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.

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.

How translational modeling in oncology needs to get the mechanism just right

Translational model‐based approaches have played a role in increasing success in the development of novel anticancer treatments. However, despite this, significant translational uncertainty remains from animal models to patients. Optimization of dose and scheduling (regimen) of drugs to maximize the therapeutic utility (maximize efficacy while avoiding limiting toxicities) is still predominately driven by clinical investigations. Here, we argue that utilizing pragmatic mechanism‐based translational modeling of nonclinical data can further inform this optimization. Consequently, a prototype model is demonstrated that addresses the required fundamental mechanisms.

Designing small molecules for therapeutic success: A contemporary perspective

Successful small-molecule drug design requires a molecular target with inherent therapeutic potential and a molecule with the right properties to unlock its potential. Present-day drug design strategies have evolved to leave little room for improvement in drug-like properties. As a result, inadequate safety or efficacy associated with molecular targets now constitutes the primary cause of attrition in preclinical development through Phase II. This finding has led to a deeper focus on target selection. In this current reality, design tactics that enable rapid identification of risk-balanced clinical candidates, translation of clinical experience into meaningful differentiation strategies, and expansion of the druggable proteome represent significant levers by which drug designers can accelerate the discovery of the next generation of medicines.

Tutorial for $DESIGN in NONMEM: Clinical trial evaluation and optimization

This NONMEM tutorial shows how to evaluate and optimize clinical trial designs, using algorithms developed in design software, such as PopED and PFIM 4.0. Parameter precision and model parameter estimability is obtained by assessing the Fisher Information Matrix (FIM), providing expected model parameter uncertainty. Model parameter identifiability may be uncovered by very large standard errors or inability to invert an FIM. Because evaluation of FIM is more efficient than clinical trial simulation, more designs can be investigated, and the design of a clinical trial can be optimized. This tutorial provides simple and complex pharmacokinetic/pharmacodynamic examples on obtaining optimal sample times, doses, or best division of subjects among design groups. Robust design techniques accounting for likely variability among subjects are also shown. A design evaluator and optimizer within NONMEM allows any control stream first developed for trial design exploration to be subsequently used for estimation of parameters of simulated or clinical data, without transferring the model to another software. Conversely, a model developed in NONMEM could be used for design optimization. In addition, the $DESIGN feature can be used on any model file and dataset combination to retrospectively evaluate the model parameter uncertainty one would expect given that the model generated the data, particularly if outliers of the actual data prevent a reasonable assessment of the variance‐covariance. The NONMEM trial design feature is suitable for standard continuous data, whereas more elaborate trial designs or with noncontinuous data‐types can still be accomplished in optimal design dedicated software like PopED and PFIM.

Population Pharmacokinetics and Pharmacodynamics of the Neutralizing Antibodies Bamlanivimab and Etesevimab in Patients With Mild to Moderate COVID-19 Infection

Bamlanivimab and etesevimab are neutralizing antibodies indicated for treatment of coronavirus disease 2019 (COVID-19) in patients with early mild or moderate disease. We present the use of pharmacokinetic/pharmacodynamic (PK/PD) modeling that characterizes the timecourse of viral load obtained from 2,970 patients from 2 phase II clinical trials. The model was used for identification of optimal doses that would result in at least 90% of patients achieving serum drug concentrations that result in 90% of maximum drug effect (IC90) for at least 28 days. The serum IC90 (95% confidence interval) was estimated to be 4.2 (3.2-4.3) µg/mL for bamlanivimab and 12.6 (9.7-12.8) µg/mL for etesevimab. Observed clinical trial data confirmed PK and PK/PD model predictions that doses of 700 mg bamlanivimab and 1,400 mg etesevimab would result in maximum reduction in viral load, with no additional effect seen at higher doses. No dose adjustment is recommended as age, sex, race, baseline viral load, and hepatic impairment did not have a significant impact on the PK of the antibodies. Earlier drug administration resulted in greater reductions in viral load, demonstrating the importance of receiving treatment as soon as possible. Relative to placebo, typical reduction in viral load over a 7-day period was estimated to be 80 or 93% (drug administered 4 days or 1 day after the onset of symptoms, respectively), P < 0.0001. PK/PD modeling and simulation was pivotal throughout the drug development and emergency use authorization process.

Pharmacometrics meets statistics-A synergy for modern drug development

Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.

Clinical diagonal translation of nanoparticles: Case studies in dendrimer nanomedicine

Among the numerous nanomedicine formulations, dendrimers have emerged as original, efficient, carefully assembled, hyperbranched, polymeric nanoparticles based on synthetic monomers. Dendrimers are used either as nanocarriers of drugs or as drugs themselves. When used as drug carriers, dendrimers are considered 'best-in-class agents', modifying and enhancing the pharmacokinetic and pharmacodynamic properties of the active entities encapsulated or conjugated with the dendrimers. When used as drugs themselves, dendrimers represent a novel category of "first-in-class" drugs. The purpose of this original review is to analyse the different strategies involved in the development, application, and impact of dendrimers as drugs. We examine a selection of nanoparticles that use multifunctional elements and demonstrate clinical multifunctionality, and we extend these principles to applications in dendrimer nanomedicine design. Finally, for practical consideration, the concepts of vertical and diagonal translation are introduced as potential strategies to facilitate dendrimer development.

Development of a mechanistic model to predict synthetic biotic activity in healthy volunteers and patients with phenylketonuria

The development of therapeutics depends on predictions of clinical activity from pre-clinical data. We have previously described SYNB1618, an engineered bacterial therapeutic (synthetic biotic) for the treatment of Phenylketonuria (PKU), a rare genetic disease that leads to accumulation of plasma phenylalanine (Phe) and severe neurological complications. SYNB1618 consumes Phe in preclinical models, healthy human volunteers, and PKU patients. However, it remains unclear to what extent Phe consumption by SYNB1618 in the gastrointestinal tract lowers plasma Phe levels in PKU patients. Here, we construct a mechanistic model that predicts SYNB1618 function in non-human primates and healthy subjects by combining in vitro simulations and prior knowledge of human physiology. In addition, we extend a model of plasma Phe kinetics in PKU patients, in order to estimate plasma Phe lowering by SYNB1618. This approach provides a framework that can be used more broadly to define the therapeutic potential of synthetic biotics.

How Physiologic Targets Can Be Distinguished from Drug-Binding Proteins

In clinical trials, some drugs owe their effectiveness to off-target activity. This and other observations raise a possibility that many studies identifying targets of drugs are incomplete. If off-target proteins are pharmacologically important, it will be worthwhile to identify them early in the development process to gain a better understanding of the molecular basis of drug action. Herein, we outline a multidisciplinary strategy for systematic identification of physiologic targets of drugs in cells. A drug-binding protein whose genetic disruption yields very similar molecular effects as treatment of cells with the drug may be defined as a physiologic target of the drug. For a drug developed with a rational approach, it is desirable to verify experimentally that a protein used for hit optimization in vitro remains the sole polypeptide recognized by the drug in a cell. SIGNIFICANCE STATEMENT: A body of evidence indicates that inactivation of many drug-binding proteins may not cause the pharmacological effects triggered by the drugs. A multidisciplinary cell-based approach can be of great value in identifying the physiologic targets of drugs, including those developed with target-based strategies.

Model-Informed Pediatric Drug Development: Application of Pharmacometrics to Define the Right Dose for Children

One of the biggest challenges in pediatric drug development is defining a safe and effective dose in pediatric populations, which span across a wide age and development range from neonates to adolescents. Model-informed drug development approaches are particularly suited to address knowledge gaps including data leveraging to increase the success of pediatric studies. Considering the often limited number of patients available for study and logistic difficulties to collect the necessary data in pediatric populations, the application of pharmacometrics and modeling and simulation techniques can improve clinical trial efficiency, increase the probability of regulatory success, and optimize therapeutic individualization in support of dedicated trials. This review describes the state of pediatric model-informed drug development to define the right dose for children and provides suggestions for future development.

Improved Decision-Making Confidence Using Item-Based Pharmacometric Model: Illustration with a Phase II Placebo-Controlled Trial

This study aimed to illustrate how a new methodology to assess clinical trial outcome measures using a longitudinal item response theory–based model (IRM) could serve as an alternative to mixed model repeated measures (MMRM). Data from the EXACT (Exacerbation of chronic pulmonary disease tool) which is used to capture frequency, severity, and duration of exacerbations in COPD were analyzed using an IRM. The IRM included a graded response model characterizing item parameters and functions describing symptom-time course. Total scores were simulated (month 12) using uncertainty in parameter estimates. The 50th (2.5th, 97.5th) percentiles of the resulting simulated differences in average total score (drug minus placebo) represented the estimated drug effect (95%CI), which was compared with published MMRM results. Furthermore, differences in sample size, sensitivity, specificity, and type I and II errors between approaches were explored. Patients received either oral danirixin 75 mg twice daily (n = 45) or placebo (n = 48) on top of standard of care over 52 weeks. A step function best described the COPD symptoms-time course in both trial arms. The IRM improved precision of the estimated drug effect compared to MMRM, resulting in a sample size of 2.5 times larger for the MMRM analysis to achieve the IRM precision. The IRM showed a higher probability of a positive predictive value (34%) than MMRM (22%). An item model–based analysis data gave more precise estimates of drug effect than MMRM analysis for the same endpoint in this one case study.

End-to-end application of model-informed drug development for ertugliflozin, a novel sodium-glucose cotransporter 2 inhibitor

Model-informed drug development (MIDD) is critical in all stages of the drug-development process and almost all regulatory submissions for new agents incorporate some form of modeling and simulation. This review describes the MIDD approaches used in the end-to-end development of ertugliflozin, a sodium-glucose cotransporter 2 inhibitor approved for the treatment of adults with type 2 diabetes mellitus. Approaches included (1) quantitative systems pharmacology modeling to predict dose-response relationships, (2) dose-response modeling and model-based meta-analysis for dose selection and efficacy comparisons, (3) population pharmacokinetics (PKs) modeling to characterize PKs and quantify population variability in PK parameters, (4) regression modeling to evaluate ertugliflozin dose-proportionality and the impact of uridine 5'-diphospho-glucuronosyltransferase (UGT) 1A9 genotype on ertugliflozin PKs, and (5) physiologically-based PK modeling to assess the risk of UGT-mediated drug-drug interactions. These end-to-end MIDD approaches for ertugliflozin facilitated decision making, resulted in time/cost savings, and supported registration and labeling.

Conditional distribution modeling as an alternative method for covariates simulation: Comparison with joint multivariate normal and bootstrap techniques

Clinical trial simulation (CTS) is a valuable tool in drug development. To obtain realistic scenarios, the subjects included in the CTS must be representative of the target population. Common ways of generating virtual subjects are based upon bootstrap (BS) procedures or multivariate normal distributions (MVNDs). Here, we investigated the performance of an alternative method based on conditional distributions (CDs). Covariate data from a hypertension drug development program were used. The methods were evaluated based on the original data set (internal evaluation) and on their ability to reproduce an older, unobserved population (extrapolation). Similar results were obtained in the internal evaluation for summary statistics, yet BS was able to preserve the correlation structure of the empirical distribution, which was not adequately reproduced by MVND; CD was in between BS and MVND. BS does not allow to extrapolate to an unobserved population. When the data set used to inform the extrapolation was well approximated by an MVND, the results from CD and MVND were comparable. However, improved extrapolation performance was observed for CD when deviations from normality assumptions occurred. If CTS is used to simulate within the observed distribution, BS is the preferred method. When extrapolating to new populations, a parametric method like CD/MVND is needed. In case the empirical multivariate distribution is characterized by linearly related covariates and unimodal marginal distributions, MVND can be used because of the simpler statistical framework and well‐established use; however, if uncertainty about the MVND assumptions exists, CD will increase the confidence in the simulations compared to MVND.

Assessing Treatment Effects with Pharmacometric Models: A New Method that Addresses Problems with Standard Assessments

Longitudinal pharmacometric models offer many advantages in the analysis of clinical trial data, but potentially inflated type I error and biased drug effect estimates, as a consequence of model misspecifications and multiple testing, are main drawbacks. In this work, we used real data to compare these aspects for a standard approach (STD) and a new one using mixture models, called individual model averaging (IMA). Placebo arm data sets were obtained from three clinical studies assessing ADAS-Cog scores, Likert pain scores, and seizure frequency. By randomly (1:1) assigning patients in the above data sets to "treatment" or "placebo," we created data sets where any significant drug effect was known to be a false positive. Repeating the process of random assignment and analysis for significant drug effect many times (N = 1000) for each of the 40 to 66 placebo-drug model combinations, statistics of the type I error and drug effect bias were obtained. Across all models and the three data types, the type I error was (5th, 25th, 50th, 75th, 95th percentiles) 4.1, 11.4, 40.6, 100.0, 100.0 for STD, and 1.6, 3.5, 4.3, 5.0, 6.0 for IMA. IMA showed no bias in the drug effect estimates, whereas in STD bias was frequently present. In conclusion, STD is associated with inflated type I error and risk of biased drug effect estimates. IMA demonstrated controlled type I error and no bias.