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During recent years there has been a surge in developing and applying physiologically based pharmacokinetic (PBPK) models in pregnant women to better understand and predict changes in drug pharmacokinetics throughout pregnancy. As a consequence, the number of publications focusing on pregnancy PBPK models has increased substantially. However, to date these models, especially across various platforms, have not been systematically evaluated. Hence, this review aims to assess published PBPK models in pregnancy used for therapeutic purposes., (© 2018 American Society for Clinical Pharmacology and Therapeutics.)

Introduction: In addition to tailored clinical trials in patients on chronic hemodialysis (HD) during drug development, clinician-initiated post-marketing studies and case reports on individual pharmacokinetic (PK) assessments provide an important source of information about drug dialysability and individualized dose recommendations in this vulnerable population. Areas covered: First, factors that may alter drug exposure in HD patients are explained. Second, available regulatory and methodological guidelines for PK assessments in this population are summarized. Third, a 4-step approach is proposed to develop individual dose recommendations for HD patients receiving drugs without data from a PK study: (1) literature search, (2) model-based dosage decisions, (3) validation and refinement through concentration monitoring, and (4) publication of relevant observations. Fourth, clinician-initiated PK assessments and case reports to evaluate and individualize use of drugs in HD patients are reviewed, and recommendations to enhance their quality are discussed. Expert commentary: Guidance on collecting and reporting PK information in individual HD patients is warranted to ensure completeness and consistency of such PK studies. A checklist and template for easy-to-implement PK calculations and pharmacometric modeling is provided to facilitate evaluation and individualization of dosing strategies in these patients. [ABSTRACT FROM AUTHOR]

The presented analysis was performed to characterize the relationship between treatment-related early (week 4) and longer term (3-6 months) weight loss to understand the potential utility of 4-week proof-of-mechanism studies in the early decision-making process during clinical development of new antiobesity compounds. A regression-based meta-analysis was performed leveraging publically available clinical outcomes data to (1) characterize the within-trial relationship between treatment-related early and longer term body weight loss and (2) identify and quantify key covariate effects on this relationship. Data from 89 randomized clinical trials with 209 treatment arms, representing observations from 54 461 patients and 9 treatments, were available for the meta-analysis. Results indicated that (1) there is a correlation between treatment-related early and longer term body weight loss ( r > 0.9), (2) baseline body weight influences the relationship between early and longer term weight loss, whereas comorbidity such as type 2 diabetes mellitus, class of drugs including GLP-1 analogues and the antiobesity compounds lorcaserin or phentermine/topiramate showed no significant effects on this relationship. The model was externally evaluated with data from the investigational compound beloranib, for which longer term weight loss could be successfully predicted based on early response data. Based on these results, the identified strong relationship between treatment-related early and longer term weight loss appears to be independent of mechanism of action. Thus, findings from this analysis can optimize design of clinical studies and facilitate development of new anti-obesity compounds. [ABSTRACT FROM AUTHOR]

Drug transporters play a key role in mediating the uptake of endo- and exogenous substances into cells as well as their efflux. Therefore, variability in drug transporter activity can influence pharmaco- and toxicokinetics and be a determinant of drug safety and efficacy. In children, particularly in neonates and young infants, the contribution of tissue-specific drug transporters to drug absorption, distribution, and excretion may differ from that in adults. In this review 5 major factors and their interdependence that may influence drug transporter activity in children are discussed: developmental differences, genetic polymorphisms, pediatric comorbidities, interacting comedication, and environmental factors. Even if data are sparse, altered drug transporter activity due to those factors have been associated with clinically relevant differences in drug disposition, efficacy, and safety in pediatric patients. Single nucleotide polymorphisms in drug transporter-encoding genes were the most studied source of drug transporter variability in children. However, in the age group where drug transporter activity has been reported to differ from that in adults, namely neonates and young infants, hardly any studies have been performed. Longitudinal studies in this young population are required to investigate the age- and disease-dependent genotype-phenotype relationships and relevance of drug transporter drug-drug interactions. Physiologically based pharmacokinetic modeling approaches can integrate drug- and patient-specific parameters, including drug transporter ontogeny, and may further improve in silico predictions of pediatric-specific pharmacokinetics. [ABSTRACT FROM AUTHOR]

The article talks about modeling and simulation (M&S) in the drug development process is an industry-proven scientific approach used to inform crucial drug development decisions. The Food and Drug Administration has shown a strong commitment to utilizing M&S in the drug development process. The article adds that simulations based on disease progression models and clinical trial trial results can predict results of the tests and have the potential to improve oncology drug development .

The article discusses various reports published within the issue, which focuses on factors that affect drug dosing, systemic exposure, efficacy, and safety in patients with chronic kidney disease.

In this article the authors discuss the importance of optimizing drug development and use in patients with chronic kidney disease. They are critical of a lack of current and clinically relevant data for guiding drug selection and dosing in patients with kidney disease which exists. They are supportive of research which is being conducted on the treatment of kidney disease and of efforts to develop effective drugs which the patients can use with kidney disease.

An introduction is presented for this issue which focuses on the field of pharmacometrics, the American Society of Pharmacometrics, and innovation.

Systematic implementation of model-based drug development (MBDD) to drug discovery and development has the potential to significantly increase the rate of medical breakthroughs and make available new and better treatments to patients. An analysis of the strengths, weaknesses, opportunities, and threats (ie, SWOT) was conducted through focus group discussions that included 24 members representing 8 pharmaceutical companies to systematically assess the challenges to implementing MBDD into the drug development decision-making process. The application of the SWOT analysis to the successful implementation of MBDD yielded 19 strengths, 27 weaknesses, 34 opportunities, and 22 threats, which support the following conclusions. The shift from empirical drug development to MBDD requires a question-based mentality; early, proactive planning; dynamic access to multisource data; quantitative knowledge integration; multidisciplinary collaboration; effective communication and leadership skills; and innovative, impactful application of pharmacometrics focused on enhancing quantitative decision making. The ultimate goal of MBDD is to streamline discovery and development of innovative medicines to benefit patients. [ABSTRACT FROM PUBLISHER]

An introduction is presented for this issue which focuses on the impact of methodological and technological innovation as well as multidisciplinary integration of research in the field of pharmacometrics.

The article comments on the planning for the American Conference on Pharmacometrics (ACoP) meetings in Tucson, Arizona in 2008 and Mystic, Connecticut in 2009, and calls on the pharmacometric community to volunteer their time and skills to the organization via the ACOP Web site which is at www.to-acop.org.

Advances in experimental medicine and technological innovation during the past century have brought tremendous progress in modern medicine and generated an ever-increasing amount of data from bench and bedside. The desire to extend scientific knowledge motivates effective data integration. Technological innovation makes this possible, which in turn accelerates the advancement in science. This mutually beneficial interaction is illustrated by the development of an expanded mechanism-based model for understanding a novel mechanism, sodium— glucose cotransporter-2 SGLT2 inhibition for potential treatment of type 2 diabetes mellitus. [ABSTRACT FROM PUBLISHER]

During the past decade, the pharmaceutical industry has seen the increasing application of pharmacometrics approaches in drug development. However, the full potential of incorporating model-based approaches in drug development and its impact on decision making has not been fully realized to date. In 2009, a survey on model-based drug development (MBDD) was conducted (1) to further understand the current state of MBDD in the pharmaceutical industry and (2) to identify opportunities to realize the full potential of MBDD. Ten large and mid-sized pharmaceutical companies provided responses to this survey. The results indicate that MBDD is achieving broad application in early and late development and is positively affecting both internal and regulatory decisions. Senior leadership (vice president and higher) within the companies indicated widely accepted utility for dose selection and gaining acceptance for study design and regulatory interactions but limited acceptance in discovery and commercial/pipeline decisions. Mounting appreciation for the impact of MBDD on internal and regulatory decision-making bodes well for the future of the pharmacometric discipline and the growth of opportunities to realize the full potential of MBDD. [ABSTRACT FROM PUBLISHER]

Model-based drug development (MBDD) is a promising approach to improve decision making in drug development. The pharmaceutical industry has made substantial progress from engaging in empirical decision making to increasingly using pharmacometrics (ie, modeling and simulation [M&S]) as a quantitative decision-making tool. Focusing on culture and an organizational structure perspective, this commentary summarizes experiences and vision from industry M&S leaders on implementing MBDD. A culture for MBDD needs to have wide acceptance of MBDD, enhanced decision making with probability-based evidence and transparent rationale, quantitative impact metrics, and a brand that emphasizes cross-disciplinary collaboration and ownership. An organizational structure for MBDD needs to have a dedicated pharmacometrics function, fine balance between quick wins and impact on long-term R&D goals, and collaborative MBDD teams among clinical pharmacologists, statisticians, pharmacometricians, and clinicians. Pharmaceutical companies with these characteristics are prepared to fully embrace and implement MBDD. [ABSTRACT FROM PUBLISHER]