Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices.

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies.

Assessing switchability for biosimilar products: modelling approaches applied to children's growth.

The present paper describes two statistical modelling approaches that have been developed to demonstrate switchability from the original recombinant human growth hormone (rhGH) formulation (Genotropin(®) ) to a biosimilar product (Omnitrope(®) ) in children suffering from growth hormone deficiency. Demonstrating switchability between rhGH products is challenging because the process of growth varies with the age of the child and across children. The first modelling approach aims at predicting individual height measured at several time-points after switching to the biosimilar.

Pharmacokinetic interactions among imatinib, bosentan and sildenafil, and their clinical implications in severe pulmonary arterial hypertension.

Aims: This study characterized the population pharmacokinetics (PK) of imatinib in patients with severe pulmonary arterial hypertension (PAH), investigated drug-drug interactions (DDI) among imatinib, sildenafil and bosentan, and evaluated their clinical implications.

Methods: Plasma concentrations of imatinib, bosentan and sildenafil were collected in a phase III study and were used to characterize the PK of imatinib in this population. DDIs among the three drugs were quantified using a linear mixed model and log-transformed drug concentrations.

Reduced sodium transport with nasal administration of the prostasin inhibitor camostat in subjects with cystic fibrosis.

Background: Prostasin, a trypsin-like serine protease, is a channel-activating protease and major regulator of epithelial sodium channel-mediated sodium absorption. Its direct inhibition by camostat represents a potential approach to inhibiting sodium transport in cystic fibrosis (CF).

Methods: To determine whether a topical formulation of camostat represents an efficacious and tolerable approach to reducing Na+ transport in the CF airway, we conducted a two-part randomized, double-blind, placebo-controlled, crossover, ascending single-dose study to evaluate the pharmacodynamics, safety, and pharmacokinetics of camostat administered through a nasal spray pump in subjects with CF.

A novel model-based approach for dose determination of glycopyrronium bromide in COPD.

Background: Glycopyrronium bromide (NVA237) is an inhaled long-acting muscarinic antagonist in development for treatment of COPD. This study compared the efficacy and safety of once-daily (OD) and twice-daily (BID) glycopyrronium bromide regimens, using a novel model-based approach, in patients with moderate-to-severe COPD.

Methods: Double-blind, randomized, dose-finding trial with an eight-treatment, two-period, balanced incomplete block design.

Efficient algorithms for optimal designs with correlated observations in pharmacokinetics and dose-finding studies.

Random effects models are widely used in population pharmacokinetics and dose-finding studies. However, when more than one observation is taken per patient, the presence of correlated observations (due to shared random effects and possibly residual serial correlation) usually makes the explicit determination of optimal designs difficult. In this article, we introduce a class of multiplicative algorithms to be able to handle correlated data and thus allow numerical calculation of optimal experimental designs in such situations.

Characterization of the bronchodilatory dose response to indacaterol in patients with chronic obstructive pulmonary disease using model-based approaches.

Background: Indacaterol is a once-daily long-acting inhaled β2-agonist indicated for maintenance treatment of moderate-to-severe chronic obstructive pulmonary disease (COPD). The large inter-patient and inter-study variability in forced expiratory volume in 1 second (FEV1) with bronchodilators makes determination of optimal doses difficult in conventional dose-ranging studies. We considered alternative methods of analysis.

Omalizumab decreases IgE production in patients with allergic (IgE-mediated) asthma; PKPD analysis of a biomarker, total IgE.

What Is Already Known About This Subject: Omalizumab is a humanized anti-IgE monoclonal antibody that binds and captures circulating IgE, preventing interaction with receptors on mast cells and basophils, thereby interrupting the allergic cascade. It has a well-characterized efficacy and safety profile in patients with asthma. While omalizumab is known to reduce serum free IgE concentrations, effects on total IgE and IgE production are less well characterized.

Evaluation of biomarkers for pharmacological activity.

In recent years the cost of drug development has increased the demands on efficiency in the selection of suitable drug candidates. Biomarkers for efficacy and safety could be a plausible strategy to improve this selection process. In the present work, we focus on the study and evaluation of different physiological variables as biomarkers for pharmacological activity.

Atomoxetine treatment of adults with ADHD and comorbid alcohol use disorders.

Objective: Adults with attention-deficit/hyperactivity disorder (ADHD) have higher rates of alcohol and drug use disorders than adults without ADHD. The study aim was to determine if atomoxetine was superior to placebo in improving ADHD and alcohol use in recently abstinent adults with ADHD and comorbid alcohol use disorder.

Methods: Adults with DSM-IV diagnoses of ADHD and alcohol abuse and/or dependence were abstinent from alcohol at least 4 days (maximum 30 days) before study randomization.

Conditional mixed models with crossed random effects.

The analysis of continuous hierarchical data such as repeated measures or data from meta-analyses can be carried out by means of the linear mixed-effects model. However, in some situations this model, in its standard form, does pose computational problems. For example, when dealing with crossed random-effects models, the estimation of the variance components becomes a non-trivial task if only one observation is available for each cross-classified level.

Accounting for variability in individual hierarchical clinical trial data.

Meta-analytical approaches have been extensively used to analyze medical data. In most cases, the data come from different studies or independent trials with similar characteristics. However, these methods can be applied in a broader sense.

Antibiotic prescribing for acute cough: the effect of perceived patient demand.

Background: GPs decide whether or not to prescribe antibiotics for acute cough. Apart from clinical signs and symptoms, non-medical reasons influence this decision as well.

Aim: To obtain a valid estimate of the effect of perceived patient demand.

Joint modelling of repeated measurements and event time: application to performance traits and survival of lambs bred in sub-humid tropics.

We considered the analysis of a study for Dorper, Red Maasai and crossbred lambs born over a period of 6 years at the Diani Estate, Kenya. The study was designed to compare survival and performance traits of genotypes with differing susceptibilities to helminthiasis. The available data include information on time to death and repeated measurements of body weight, packed cell volume (PCV) and faecal egg count (FEC) of the animals.

Applying concepts of generalizability theory on clinical trial data to investigate sources of variation and their impact on reliability.

This work aims at applying concepts of generalizability theory to data resulting from clinical trials. The focus is to study the sources of variance and their impact on the reliability and generalizability of a psychiatric measurement scale. The goal is to identify, measure, and thereby potentially find strategies to reduce the influence of these sources on the measurement in question for future trials.

Prentice's approach and the meta-analytic paradigm: a reflection on the role of statistics in the evaluation of surrogate endpoints.

We put a perspective on the strengths and limitations of statistical methods for the evaluation of surrogate endpoints. Whereas using several trials overcomes some of the limitations of a single-trial framework (Prentice, 1989, Statistics in Medicine 8, 431-440), arguably the evaluation of surrogate endpoints can never be done using only statistical evidence but such evidence should be seen as but one component in a decision-making process that involves, among others, a number of clinical and biological considerations. We briefly present a hierarchical framework that incorporates ideas from Prentice's work and is uniformly applicable to different types of surrogate and true clinical outcomes.

Applying linear mixed models to estimate reliability in clinical trial data with repeated measurements.

Repeated measures are exploited to study reliability in the context of psychiatric health sciences. It is shown how test-retest reliability can be derived using linear mixed models when the scale is continuous or quasi-continuous. The advantage of this approach is that the full modeling power of mixed models can be used.

Statistical challenges in the evaluation of surrogate endpoints in randomized trials.

The validation of surrogate endpoints has been studied by Prentice, who presented a definition as well as a set of criteria that are equivalent if the surrogate and true endpoints are binary. Freedman et al. supplemented these criteria with the so-called proportion explained.