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During development of a drug, the requirement of evaluating the proarrhythmic risk and delayed repolarization needs to be fulfilled. Would it be possible to create an alternative to a thorough QT (TQT) study or is there a need to perform a dedicated TQT study? How is an alternative approach generated, what information is available, and which instructions are considered missing today to generate such an approach? This tutorial describes the considerations and path followed to create an early and feasible alternative to a TQT study using experience‐based insights from a successful application to the US Food and Drug Administration for GLPG1972, an ADAMTS‐5 inhibitor, and discusses the approach used in light of the current guidelines and literature. [ABSTRACT FROM AUTHOR]

Clinical assessment of drug–drug interactions (DDIs) in children is not a common practice in drug development. Therefore, physiologically‐based pharmacokinetic (PBPK) modeling can be beneficial for informing drug labeling. Using ivabradine and its metabolite (both cytochrome P450 3A4 enzyme (CYP3A4) substrates), the objectives were (i) to scale ivabradine‐metabolite adult PBPK/PD to pediatrics, (ii) to predict the DDIs with a strong CYP3A4 inhibitor, and (iii) to compare the sensitivity of children to DDIs using two CYP3A4 hepatic ontogeny functions: Salem and Upreti. A scaled parent‐metabolite PBPK/PD model from adults to children satisfactorily predicted pharmacokinetics (PK) and pharmacodynamics (PD) in 74 children (0.5–18 years) regardless of CYP3A4 hepatic ontogeny function applied. However, using the Salem ontogeny, mean predicted parent and metabolite area under the concentration‐time curve over 12 hours (AUC12h) and heart rate change from baseline were 2‐fold, 1.5‐fold, and 1.4‐fold higher in young children (0.5–3 years old) compared with Upreti ontogeny, respectively. Despite these differences, choice of appropriate hepatic CYP3A4 ontogeny was challenging due to sparse PK and PD data. Different sensitivity to ivabradine–ketoconazole DDIs was simulated in young children relative to adults depending on the choice of hepatic CYP3A4 ontogeny. Predicted ivabradine and metabolite AUCDDI/AUCcontrol were 2‐fold lower in the youngest children (0.5–1 year old) compared with adults (Salem function). In contrast, the Upreti function predicted comparable ivabradine DDIs across all age groups, although predicted metabolite AUCDDI/AUCcontrol was 1.3‐fold higher between the youngest children and adults. In the case of PD, differences in predicted DDIs were minor across age groups and between both functions. Current work highlights the importance of careful consideration of hepatic CYP3A4 ontogeny function and implications on labeling recommendations in the pediatric population. [ABSTRACT FROM AUTHOR]

The blood–brain barrier (BBB) is a highly selective membrane composed predominantly of brain capillary endothelial cells expressing drug efflux transporters that prevent substrates from accessing the brain. Inflammation is associated with central nervous system diseases and can impair BBB permeability via several mechanisms, including altered transporter and cell junction expression. This can modify the brain's exposure to drugs. However, comprehensive genomic analysis of the impact of interleukin (IL)‐6, which plays a key role in the inflammatory response, on the BBB is lacking. In the present study, we analyzed the effects of exposure of hCMEC/D3 cells to 20 ng/mL IL‐6 for 72 h. We performed RNA sequencing and ABC transporter efflux assays. Physiologically based pharmacokinetics (PBPK) simulations were conducted to evaluate the potential impact of IL‐6 on the digoxin pharmacokinetics profile and brain exposure by decreasing BBB ABCB1 efflux activity. Exposure of hCMEC/D3 cells to IL‐6 triggered the deregulation of numerous genes involved in barrier permeability, such as cell junctions, focal adherens complex, and cell adhesion molecules. We observed mild modification of the mRNA expression and efflux activities of ABC transporters. PBPK simulation showed that, if we only consider the impact of IL‐6 on ABCB1 transporter, the modification of the digoxin pharmacokinetics profile and brain exposure is slight. IL‐6 slightly affected the gene expression levels and activities of ABC transporters on BBB cells, exhibiting a weaker effect than on hepatic cells. However, inflammation may cause other modifications, such as altered BBB permeability, that could modify drug pharmacokinetics. [ABSTRACT FROM AUTHOR]

Transforming Translation Through Quantitative Pharmacology for High-Impact Decision Making in Drug Discovery and Development This perspective emphasizes the role of quantitative translational pharmacology to bridge science and practice to make better, faster, and efficient decisions in drug discovery and development. Furthermore, a QSP framework for drug-target interactions can be particularly valuable for engineering molecules in drug discovery settings when dealing with complex modalities such as bispecific antibodies or drug delivery systems. [Extracted from the article]

Introduction: In this paper, we studied the effect over time of agomelatine, an antidepressant drug administered in patient with major depressive disorder, through item response theory (IRT), taking into account a strong placebo effect and missing not at random. We also assessed the informativeness of the HAMD-17 scale's item. Materials and Methods: The data includes five phase III clinical trials sponsored by Servier Institute, totalling 1549 patients followed during a maximum of 1 year. At each observation, individual scores for the 17 items of the HAMD scale were recorded. The probability for each score was modelled with IRT. A non-linear mixed effects model was used to describe the evolution of the disease and was coupled with a time to event model to predict dropout. Clinical trial simulations were then used to compare placebo and active treatment. Informativeness of each item was evaluated using the Fisher information theory. Results: The best model combined an IRT model, a longitudinal model for underlying depression which describes the remission and then a possible relapse, and a hazard model for dropout depending on the evolution from baseline. The drug effect was best modelled as an effect on the remission and the relapse phases. The median predicted drop in HAMD between baseline and 6 weeks was 8.8 (90% PI, 8.3–9.2) when on placebo and 13.1 (90% PI, 12.8–13.4) when treated. Nine items were found to be the most informative. Conclusion: The IRT framework allowed to characterise the evolution of depression with time and estimate the effect of agomelatine, as well as the link between symptoms and disease. [ABSTRACT FROM AUTHOR]