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The opportunistic fungal infection cryptococcal meningoencephalitis is a major cause of death among people living with HIV in sub‐Saharan Africa. We report pharmacokinetic (PK) and safety data from a randomized, four‐period crossover phase I trial of three sustained‐release (SR) oral pellet formulations of 5‐flucytosine conducted in South Africa. These formulations were developed to require less frequent administration, to provide a convenient alternative to the current immediate release (IR) formulation, A. Formulations B, C, and D were designed to release 5‐flucytosine as a percentage of the nominal dose in vitro. We assessed their safety and PK profiles in a single dose (1 × 3000 mg at 0 h), relative to commercial IR tablets (Ancotil 500 mg tablets; 3 × 500 mg at 0 h and 3 × 500 mg at 6 h) in healthy, fasted participants. Forty‐two healthy participants were included. All treatments were well‐tolerated. The primary PK parameters, maximum observed plasma concentration (Cmax) and area under the concentration‐time profiles, were significantly lower for the SR formulations than for the IR tablets, and the geometric mean ratios fell outside the conventional bioequivalence limits. The median maximum time to Cmax was delayed for the SR pellets. Physiologically‐based PK modeling indicated a twice‐daily 6400 mg dose of SR formulation D in fasted condition would be optimal for further clinical development. This regimen is predicted to result in a rapid steady‐state plasma exposure with effective and safe trough plasma concentration and Cmax values, within the therapeutic boundaries relative to plasma exposure after four times per day administration of IR tablets (PACTR202201760181404). [ABSTRACT FROM AUTHOR]

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]

MET receptor tyrosine kinase inhibitors (TKIs) can restore sensitivity to gefitinib, a TKI targeting epidermal growth factor receptor (EGFR), and promote apoptosis in non‐small cell lung cancer (NSCLC) models resistant to gefitinib treatment in vitro and in vivo. Several novel MET inhibitors are currently under study in different phases of development. In this work, a novel tumor‐in‐host modeling approach, based on the Dynamic Energy Budget (DEB) theory, was proposed and successfully applied to the context of poly‐targeted combination therapies. The population DEB‐based tumor growth inhibition (TGI) model well‐described the effect of gefitinib and of two MET inhibitors, capmatinib and S49076, on both tumor growth and host body weight when administered alone or in combination in an NSCLC mice model involving the gefitinib‐resistant tumor line HCC827ER1. The introduction of a synergistic effect in the combination DEB‐TGI model allowed to capture gefitinib anticancer activity enhanced by the co‐administered MET inhibitor, providing also a quantitative evaluation of the synergistic drug interaction. The model‐based comparison of the two MET inhibitors highlighted that S49076 exhibited a greater anticancer effect as well as a greater ability in restoring sensitivity to gefitinib than the competitor capmatinib. In summary, the DEB‐based tumor‐in‐host framework proposed here can be applied to routine combination xenograft experiments, providing an assessment of drug interactions and contributing to rank investigated compounds and to select the optimal combinations, based on both tumor and host body weight dynamics. Thus, the combination tumor‐in‐host DEB‐TGI model can be considered a useful tool in the preclinical development and a significant advance toward better characterization of combination therapies. [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]

Quantitative pharmacology (QP) applications in translational medicine, drug‐development, and therapeutic use were crowd‐sourced by the ASCPT Impact and Influence initiative. Highlighted QP case studies demonstrated faster access to innovative therapies for patients through 1) rational dose selection for pivotal trials; 2) reduced trial‐burden for vulnerable populations; or 3) simplified posology. Critical success factors were proactive stakeholder engagement, alignment on the value of model‐informed approaches, and utilizing foundational clinical pharmacology understanding of the therapy. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the contribution of norfloxacin blood-brain barrier (BBB) transport to its delayed electroencephalogram (EEG) effect in rats. Norfloxacin was injected as a bolus dose of 150mg kg-1. Blood samples were collected for total norfloxacin plasma concentration measurements. The corresponding unbound levels were determined in brain extracellular fluid (ECF) using microdialysis. Quantitative EEG recording was conducted during 9 h post-dose. Brain ECF norfloxacin concentrations were much lower than plasma levels (AUC ratio = 9.7 ± 2.8%) but peaked very early, and concentration versus time profiles were parallel in both biological fluids. The best pharmacokinetic (PK) modelling was obtained by considering that ECF concentrations were part of the central compartment, with a proportionality factor. The peak of EEG effect was delayed and the effect versus plasma concentration curves exhibited a dramatic hysteresis. A PK-pharmacodynamic (PD) effect compartment model with a spline function to describe the relationship between effect and concentration at the effect site successfully described the data. Comparisons of PK-PD parameters estimated from plasma and ECF concentrations show that most of the delayed norfloxacin EEG effect is not due to BBB transport, but also that PD parameters derived from plasma data must be carefully interpreted when drug distribution at the effect site is restricted, as may often be the case for centrally acting drugs. [ABSTRACT FROM AUTHOR]