Pharmacogenetic (PGx) guidelines for the precise dosing and selection of drugs remain poorly implemented in current clinical practice. Among the barriers to the implementation process is the lack of clinical decision support system (CDSS) tools to aid health providers in managing PGx information in the clinical context. The present study aimed to describe the first Italian endeavor to develop a PGx CDSS, called FARMAPRICE. FARMAPRICE prototype was conceived for integration of patient molecular data into the clinical prescription process in the Italian Centro di Riferimento Oncologico (CRO)-Aviano Hospital. It was developed through a coordinated partnership between two high-tech companies active in the computerization of the Italian healthcare system. Introducing FARMAPRICE into the clinical setting can aid physicians in prescribing the most efficacious and cost-effective pharmacological therapy available.

The need for a framework to justify that a model has sufficient credibility to be used as a basis for internal or external (typically regulatory) decision-making is a primary concern when using modelling and simulation (M&S) in healthcare. This chapter reviews published standards on verification, validation, and uncertainty quantification (VVUQ) as well as regulatory guidance that can be used to establish model credibility in this context, providing a potential starting point for a globally harmonised model credibility framework.

Cardiac safety assessment is an integral part of drug discovery and development. Drug candidates that adversely affect cardiac or hemodynamic function should be discontinued early unless a favorable benefit-risk ratio for patients can be justified. In this hypothesis-generating work, we aimed to develop a conceptual framework for informing early safety risk assessment based on in vitro drug affinities to pharmacological targets. For illustration, we used the drug-induced cardiotoxicity rank (DICTrank) data comprising 1318 drugs with cardiac safety concerns according to FDA labeling. The data was enriched with information on affinity to the most plausible mechanistic targets, clinical drug exposure, and human plasma protein binding. We descriptively identified 18 target classes potentially associated with elevated cardiovascular risk: potassium channels (accounting alone for 20% of the ‘most concern’ safety group); adrenergic, dopamine, serotonin, androgen, sex hormone, and opioid receptors; cyclooxygenase; sodium and calcium channels; muscarinic and glucocorticoid receptors; phosphodiesterase; topoisomerase; angiotensin-converting enzyme; angiotensin II type 1 receptor; monoamine transporters, and acetylcholinesterase. Overall, 80% of the ‘most concern’ drugs compared with only 12% of the ‘no concern’ drugs were associated with these targets in this exploratory descriptive analysis. Concentration–response analyses revealed differences in target potency and free drug exposure that appeared associated with variability in the severity of cardiotoxicity among drugs acting on the same target. This framework demonstrates how in vitro data can be used to benchmark new compounds early in development, enabling the timely discontinuation of candidates associated with substantial risk.

The target‐engagement predictor, implemented within the InSilicoTrials cloud‐based, AI‐powered platform for model‐informed drug discovery and development, enables early exploration of time‐dependent pharmacological responses. Predictions can be generated using inputs ranging from molecular structure to in vitro, animal, and clinical data. Scaling and translation across species are supported, accounting for differences in body size and plasma protein binding. This tutorial demonstrates the application of the target‐engagement predictor for benchmarking and decision‐support in the evaluation of new drug candidates. Pharmacokinetic (PK) and pharmacodynamic (PD) data for carvedilol, ivabradine, and sunitinib were used as illustrative examples. The ANDROMEDA and PLATO software were used to predict human clinical PK and target affinity parameters, respectively, based on molecular SMILES representations of the drugs. These predictions were subsequently incorporated into longitudinal PKPD simulation models. User‐friendly input wizards and interactive results dashboards were developed to facilitate clinical trial simulation for broader project teams, without requiring programming expertise. Predictions derived from molecular structure were compared with data generated from in vitro, rat, and clinical experimental settings. The target‐engagement predictor enables integration of heterogenous data sources to support drug development decisions, including early benefit–risk assessment and dose selection for Phase 1 studies. Direct prediction based on molecular structure alone represents a promising approach for early‐stage screening and evaluation of new drug candidates.

QT interval prolongation is associated with an increased risk of torsades de pointes (TdP), a potentially life-threatening ventricular arrhythmia, making assessment of cardiac repolarization a critical component of drug development. This work demonstrates how integrated in silico methodologies can be used to predict and quantitatively characterize QT liability across the drug discovery and development continuum. A proof-of concept translational workflow combining molecular structure analysis, in vitro pharmacology, animal data, and clinical exposure-response modeling was evaluated using the retrospective case examples verapamil and dofetilide. Molecular structure- and pharmacology-based assessments predicted hERG channel interaction and potential QT risk for both compounds. While initial in silico analyses suggested a high QT liability for verapamil and dofetilide, mechanistic electrophysiological modeling differentiated their effects. For verapamil, concurrent calcium channel inhibition offset hERG-mediated effects, resulting in predicted reductions in action potential duration and no clinically relevant QT prolongation. In contrast, dofetilide produced concentration-dependent increases in action potential duration, consistent with its known proarrhythmic potential. Translation of nonclinical findings to humans accurately predicted the absence of clinically meaningful QT effects for verapamil and identified QT prolongation exceeding the regulatory 10 ms threshold for dofetilide at therapeutic concentrations. Drug exposure and pharmacokinetic properties could be anticipated based solely on molecular structure. This conceptual workflow demonstrates how integrated quantitative in silico approaches can support earlier identification, characterization, and communication of QT risk, enabling informed decision-making and improved development efficiency from bench to bedside.