Integration of Pharmacometric and Statistical Analyses Using Clinical Trial Simulations to Enhance Quantitative Decision Making in Clinical Drug Development

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.

Longitudinal Meta-Analysis of Historical Parkinson's Disease Trials to Inform Future Trial Design

BACKGROUND

The outcome of clinical trials in neurodegeneration can be highly uncertain due to the presence of a strong placebo effect.

OBJECTIVES

To develop a longitudinal model that can enhance the success of future Parkinson's disease trials by quantifying trial-to-trial variations in placebo and active treatment response.

METHODS

A longitudinal model-based meta-analysis was conducted on the total score of Unified Parkinson's Disease Rating Scale (UPDRS) Parts 1, 2, and 3. The analysis included aggregate data from 66 arms (observational [4], placebo [28], or investigational-drug-treated [34]) from 4 observational studies and 17 interventional trials. Inter-study variabilities in key parameters were estimated. Residual variability was weighted by the size of study arms.

RESULTS

The baseline total UPDRS was estimated to average at 24.5 points. Disease score was estimated to worsen by 3.90 points/year for the duration of the treatments; whilst notably, arms with a lower baseline progressed faster. The model captured the transient nature of the placebo response and sustained symptomatic drug effect. Both placebo and drug effects peaked within 2 months; although, 1 year was needed to observe the full treatment difference. Across these studies, the progression rate varied by 59.4%, the half-life for offset of placebo response varied by 79.4%, and the amplitude for drug effect varied by 105.3%.

CONCLUSION

The longitudinal model-based meta-analysis describes UPDRS progression rate, captures the dynamics of the placebo response, quantifies the effect size of the available therapies, and sets the expectation of uncertainty for future trials. The findings provide informative priors to enhance the rigor and success of future trials of promising agents, including potential disease modifiers. © 2023 GSK. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Concept and application of the probability of pharmacological success (PoPS) as a decision tool in drug development: a position paper

BACKGROUND

In drug development, few molecules from a large pool of early candidates become successful medicines after demonstrating a favourable benefit-risk ratio. Many decisions are made along the way to continue or stop the development of a molecule. The probability of pharmacological success, or PoPS, is a tool for informing early-stage decisions based on benefit and risk data available at the time.

RESULTS

The PoPS is the probability that most patients can achieve adequate pharmacology for the intended indication while minimising the number of subjects exposed to safety risk. This probability is usually a function of dose; hence its computation typically requires exposure-response models for pharmacology and safety. The levels of adequate pharmacology and acceptable risk must be specified. The uncertainties in these levels, in the exposure-response relationships, and in relevant translation all need to be identified. Several examples of different indications are used to illustrate how this approach can facilitate molecule progression decisions for preclinical and early clinical development. The examples show that PoPS assessment is an effective mechanism for integrating multi-source data, identifying knowledge gaps, and forcing transparency of assumptions. With its application, translational modelling becomes more meaningful and dose prediction more rigorous. Its successful implementation calls for early planning, sound understanding of the disease-drug system, and cross-discipline collaboration. Furthermore, the PoPS evolves as relevant knowledge grows.

CONCLUSION

The PoPS is a powerful evidence-based framework to formally capture multiple uncertainties into a single probability term for assessing benefit-risk ratio. In GSK, it is now expected for governance review at all early-phase decision gates.

Variability and uncertainty: interpretation and usage of pharmacometric simulations and intervals

Variability and estimation uncertainty are important sources of variation in pharmacometric simulations. Different combinations of uncertainty and the variability components lead to a variety types of simulation intervals, and many realized and unrealized confusions exist among pharmacometricians on their interpretation and usage. This commentary aims to clarify some of the important underlying concepts and provide a convenient guideline on pharmacometric simulation conduct and interpretation.

Bayesian sample size determination for diagnostic accuracy studies

The development of a new diagnostic test ideally follows a sequence of stages which, among other aims, evaluate technical performance. This includes an analytical validity study, a diagnostic accuracy study, and an interventional clinical utility study. In this article, we propose a novel Bayesian approach to sample size determination for the diagnostic accuracy study, which takes advantage of information available from the analytical validity stage. We utilize assurance to calculate the required sample size based on the target width of a posterior probability interval and can choose to use or disregard the data from the analytical validity study when subsequently inferring measures of test accuracy. Sensitivity analyses are performed to assess the robustness of the proposed sample size to the choice of prior, and prior‐data conflict is evaluated by comparing the data to the prior predictive distributions. We illustrate the proposed approach using a motivating real‐life application involving a diagnostic test for ventilator associated pneumonia. Finally, we compare the properties of the approach against commonly used alternatives. The results show that, when suitable prior information is available, the assurance‐based approach can reduce the required sample size when compared to alternative approaches.

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.

Quantifying the probability of pharmacological success to inform compound progression decisions

The Probability of Pharmacology Success, or PoPS, is a powerful metric to inform progression decisions by quantifying a compound’s overall pharmacological strength based on its mechanism. It is defined as the probability that X level of pharmacology is achieved in Y proportion of patients at a safe dose. The importance of adequate drug exposure, target engagement and functional pharmacology for enabling a compound’s efficacy is widely recognized. The PoPS estimates how well these conditions are met by integrating the compound’s pharmacological properties and the target’s modulation needs for the intended indication, in a pharmacometric model that includes the knowledge uncertainty. We use examples to illustrate how it can be used to compare drug candidates under specified benefit and risk conditions, support first-in-human decisions based on exposure limits, advise preclinical lead optimisation, and define clinical-trial populations.

Application of Trial Simulation in the Design of a Prospective Study for Concentration-QTc Analysis in Support of a Thorough QT Study Waiver

The concentration-QTc (C-QTc) analysis is often applied in the first-in-human (FIH) study to demonstrate the absence of a QTc effect in support of a TQT waiver. However, a C-QTc analysis without properly designed sensitivity could fail to conclude the absence of a QTc effect at high concentrations, even though the compound is QTc negative. This is because the 90% confidence interval (CI) of the model-derived ∆∆QTc grows wider with increasing concentration, and the upper-bound could cross the 10-ms threshold, even though the slope is close to 0. So far, there is no simple math formula to calculate the sensitivity/specificity of a C-QTc analysis. A PK/QTc trial simulation scheme was applied to optimize the design features of a C-QTc trial in FIH studies by evaluating the study's sensitivity over a wide concentration range, circumventing the problem of not knowing the target concentration during FIH studies. It was also used to ensure that the specificity of the trial was well-controlled. Simulation showed that the study sensitivity can be quantitatively gauged by optimizing the dose range, the number of samples per subjects or subject number, and by sampling around Tmax, and at steady-state. The specificity of the trial can also be evaluated with this approach, and it is important to combine model-derived ∆∆QTc and slope estimate in the evaluation. The trial simulation approach helps maximize the probability of success of C-QTc analyses in FIH studies intended to support a TQT waiver.

Assessing parameter uncertainty in small-n pharmacometric analyses: value of the log-likelihood profiling-based sampling importance resampling (LLP-SIR) technique

Assessing parameter uncertainty is a crucial step in pharmacometric workflows. Small datasets with ten or fewer subjects appear regularly in drug development and therapeutic use, but it is unclear which method to assess parameter uncertainty is preferable in such situations. The aim of this study was to (i) systematically evaluate the performance of standard error (SE), bootstrap (BS), log-likelihood profiling (LLP), Bayesian approaches (BAY) and sampling importance resampling (SIR) to assess parameter uncertainty in small datasets and (ii) to evaluate methods to provide proposal distributions for the SIR. A simulation study was conducted and the 0-95% confidence interval (CI) and coverage for each parameter was evaluated and compared to reference CIs derived by stochastic simulation and estimation (SSE). A newly proposed LLP-SIR, combining the proposal distribution provided by LLP with SIR, was included in addition to conventional SE-SIR and BS-SIR. Additionally, the methods were applied to a clinical dataset. The determined CIs differed substantially across the methods. The CIs of SE, BS, LLP and BAY were not in line with the reference in datasets with ≤ 10 subjects. The best alignment was found for the LLP-SIR, which also provided the best coverage results among the SIR methods. The best overall results regarding the coverage were provided by LLP and BAY across all parameters and dataset sizes. To conclude, the popular SE and BS methods are not suitable to derive parameter uncertainty in small datasets containing ≤ 10 subjects, while best performances were observed with LLP, BAY and LLP-SIR.

Population Pharmacokinetics and Exposure-Response Modeling Analyses of Ustekinumab in Adults With Moderately to Severely Active Ulcerative Colitis

To characterize the pharmacokinetics (PK) and exposure-response (E-R) relationship of ustekinumab, an anti-interleukin-12/interleukin-23 (IL-12/IL-23) human monoclonal antibody, in the treatment of moderately to severely active ulcerative colitis (UC), population PK and E-R modeling analyses were conducted based on the data from the pivotal phase 3 induction and maintenance studies in UC patients. The observed serum concentration-time data of ustekinumab were adequately described by a 2-compartment linear PK model with first-order absorption and first-order elimination. Body weight, baseline serum albumin, sex, and antibodies to ustekinumab were the covariates to influence ustekinumab PK, but the magnitudes of the effects of these covariates were not considered clinically relevant, and dose adjustment was not warranted. Positive E-R relationships were demonstrated between ustekinumab exposure metrics and clinical endpoints (including clinical response, clinical remission, and endoscopic healing based on Mayo score) at induction week 8 and maintenance week 44, consistent with the effectiveness of ustekinumab in the induction and maintenance treatment of patients with UC. E-R modeling results suggest that ustekinumab ∼6 mg/kg intravenous induction and 90-mg subcutaneous every-8-week maintenance dose would produce greater efficacy than the 130 mg intravenous induction and the 90-mg subcutaneous every-12-week maintenance regimen, respectively. Our work provides a comprehensive evaluation of ustekinumab PK and E-R in a modeling framework to support ustekinumab dose recommendations in patients with UC.