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The article "In memoriam Lambertus (\"Bert\") A. Peletier 29 March 1937–16 December 2023: Furthering quantitative pharmacology through applied mathematics" published in CPT: Pharmacometrics & Systems Pharmacology honors the life and contributions of Lambertus (Bert) A. Peletier, a respected mathematician known for his work in partial differential equations. Peletier's career spanned from academia to mathematical pharmacology, where he applied advanced mathematical concepts to PK–PD modeling, impacting drug discovery and development. His mentorship and teaching style emphasized the importance of mathematical rigor and cross-disciplinary collaboration, leaving a lasting legacy in the scientific community. [Extracted from the article]

Pharmacogenetic testing, which involves using an individual's genetic information to determine the most effective drug and dose for them, has been rapidly adopted in academic healthcare centers in the United States and Europe. However, there is still resistance to this type of testing among healthcare providers due to factors such as cost, lack of education, and skepticism about its benefits. A survey of clinical pharmacologists found that while many believed pharmacogenetic testing was useful, they felt that more research and clinical trials were needed before widespread implementation. The survey also highlighted the need for diversity in research and testing populations to ensure equitable healthcare outcomes. Overall, there is support for the implementation of pharmacogenetic testing, but more research and education are needed. [Extracted from the article]

The article discusses the importance of getting the dosage right in clinical pharmacology, particularly in the era of precision medicine. It emphasizes the need to tailor dosage regimens to individual patients rather than using a one-size-fits-all approach. The article covers various topics related to dosage optimization, including oncology dosage, individualizing dosing for biologics, modeling and simulation techniques, and the use of quantitative clinical pharmacology methods. It also explores the challenges and advancements in dosing strategies, the role of physiologically-based pharmacokinetic modeling, and the potential of machine learning and model-informed decision-making. The article concludes by highlighting the importance of multidisciplinary efforts and collaboration in improving dosage optimization in drug development, regulatory approval, and clinical practice. [Extracted from the article]

Model-informed drug development (MIDD) and the MIDD Paired Meeting Pilot Program have emerged as strategic approaches to using computational models and simulations to inform drug development and regulatory decision-making. MIDD began with the FDA Modernization Act in 1997, which allowed for greater flexibility in drug approval and the incorporation of computational models. The FDA has since issued guidance and launched initiatives to promote model-based drug development and facilitate interactions between regulatory scientists and pharmaceutical sponsors. MIDD has been recognized by the industry and the FDA, and efforts are being made to expand and enhance its use in drug development. The FDA is also committed to international collaboration through its participation in the International Council for Harmonization. Overall, MIDD aims to improve the efficiency and effectiveness of drug development and regulatory decision-making, benefiting both the industry and patients. [Extracted from the article]

Nonracemic amisulpride (SEP‐4199) is an investigational 85:15 ratio of aramisulpride to esamisulpride and currently in clinical trials for the treatment of bipolar depression. During testing of SEP‐4199, a pharmacokinetic/pharmacodynamic (PK/PD) disconnect was discovered that prompted the development of a controlled‐release (CR) formulation with improved therapeutic index for QT prolongation. Observations that supported the development of a CR formulation included (i) plasma concentrations of amisulpride enantiomers were cleared within 24‐hours, but brain dopamine D2 receptor (D2R) occupancies, although achieving stable levels during this time, required 5 days to return to baseline; (ii) nonracemic amisulpride administered to non‐human primates produced significantly greater D2R occupancies during a gradual 6‐hour administration compared with a single bolus; (iii) concentration–occupancy curves were left‐shifted in humans when nonracemic amisulpride was gradually administered over 3 and 6 hours compared with immediate delivery; (iv) CR solid oral dose formulations of nonracemic amisulpride were able to slow drug dissolution in vitro and reduce peak plasma exposures in vivo in human subjects. By mathematically solving for a drug distribution step into an effect compartment, and for binding to target receptors, the discovery of a novel PK/PD model (termed here as Distribution Model) accounted for hysteresis between plasma and brain, a lack of receptor saturation, and an absence of accumulation of drug occupancy with daily doses. The PK/PD disconnect solved by the Distribution Model provided model‐informed drug development to continue in Phase III using the non‐bioequivalent CR formulation with diminished QT prolongation as dose‐equivalent to the immediate release (IR) formulation utilized in Phase II. [ABSTRACT FROM AUTHOR]

This article discusses the importance of dose optimization in oncology drug development and the efforts of scientific organizations, such as the American Society for Clinical Pharmacology and Therapeutics (ASCPT), in addressing this issue. It presents the findings of a survey on oncology dose optimization, including challenges and preferred approaches for dose selection. The article also explores translational approaches to dose optimization and highlights the value of innovative biomarkers in guiding treatment decisions. It emphasizes the need for collaboration among different stakeholders and suggests that a patient-centric approach and quantitative, model-informed approaches can improve clinical trial design and individualize dosing. The article draws parallels between oncology and HIV drug discovery and encourages further research in oncology dose optimization. Additionally, the document provides a list of references to various articles and studies related to clinical pharmacology and therapeutic approaches in oncology, offering diverse perspectives and approaches to inform research and development in the field. [Extracted from the article]

The article discusses the use of thorough QT studies (T-QTs) in drug development programs to assess the risk of drug-induced QT prolongation, a potential cardiac side effect. While T-QTs have been successful in identifying drugs with QT prolongation risk, they are expensive to conduct. New guidelines have been introduced to provide alternative, cheaper pathways for evaluating proarrhythmic risk. However, T-QTs are still being performed in approximately 50% of drug development programs. The article also explores the need for clinical QT assessment for large targeted proteins and novel biological modalities, suggesting that the risk of QT prolongation for these drugs is low and minimal clinical evaluation may be sufficient. [Extracted from the article]

Aims: Bedaquiline, pretomanid and linezolid (BPaL) combination treatment against Mycobacterium tuberculosis is promising, yet safety and adherence concerns exist that motivate exploration of alternative dosing regimens. We developed a mechanistic modelling framework to compare the efficacy of the current and alternative BPaL treatment strategies. Methods: Pharmacodynamic models for each drug in the BPaL combination treatment were developed using in vitro time‐kill data. These models were combined with pharmacokinetic models, incorporating body weight, lesion volume, site‐of‐action distribution, bacterial susceptibility and pharmacodynamic interactions to assemble the framework. The model was qualified by comparing the simulations against the observed clinical data. Simulations were performed evaluating bedaquiline and linezolid approved (bedaquiline 400 mg once daily [QD] for 14 days followed by 200 mg three times a week, linezolid 1200 mg QD) and alternative dosing regimens (bedaquiline 200 mg QD, linezolid 600 mg QD). Results: The framework adequately described the observed antibacterial activity data in patients following monotherapy for each drug and approved BPaL dosing. The simulations suggested a minor difference in median time to colony forming unit (CFU)‐clearance state with the bedaquiline alternative compared to the approved dosing and the linezolid alternative compared to the approved dosing. Median time to non‐replicating‐clearance state was predicted to be 15 days from the CFU‐clearance state. Conclusions: The model‐based simulations suggested that comparable efficacy can be achieved using alternative bedaquiline and linezolid dosing, which may improve safety and adherence in drug‐resistant tuberculosis patients. The framework can be utilized to evaluate treatment optimization approaches, including dosing regimen and duration of treatment predictions to eradicate both replicating‐ and non‐replicating bacteria from lung and lesions. [ABSTRACT FROM AUTHOR]

This article discusses the importance of validation in quantitative modeling and quantitative systems pharmacology (QSP) in the context of drug development and regulatory submissions. QSP models aim to improve drug development success rates by extrapolating to untested clinical scenarios, but they are associated with uncertainty and require different validation approaches than pharmacometrics. The authors propose a risk-based credibility framework for determining the level of validation required for different quantitative applications based on the potential risk to patients. They provide examples of how QSP models have been used in regulatory decisions, such as assessing the safety of insulin drugs and predicting immunogenicity of biotherapeutics and vaccines. The article emphasizes the need for fit-for-purpose validation approaches that balance confidence in model predictions with the cost and time involved in validation, as well as the importance of risk-based confidence requirements that are independent of the modeling method used. [Extracted from the article]