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Statistical Approaches in the Development of Digital Therapeutics
Published in Oleksandr Sverdlov, Joris van Dam, Digital Therapeutics, 2023
Oleksandr Sverdlov, Yevgen Ryeznik, Sergei Leonov, Valerii Fedorov
Pharmacometrics (PMx) is a scientific discipline that integrates drug, disease, and trial information to facilitate model-informed drug development and/or regulatory decisions (Williams and Ette, 2007). The PMx approach involves mathematical modeling of pharmacological and biological processes to link dose effects on drug concentration and drug response over time. Pharmacokinetic (PK) and pharmacodynamic (PD) relationships are frequently described by systems of differential equations, solutions to which are nonlinear with respect to model parameters. Pharmacometricians utilize nonlinear mixed-effects models that account for measurement errors and between-subject variability (Lindstrom and Bates, 1990). There are different PMx modeling approaches, such as physiologically-based pharmacokinetic models (Jones and Rowland-Yeo, 2013), quantitative systems pharmacology models (Bai et al., 2020), to name a few. A major advantage of these approaches is that they model dynamic phenomena of interest at the population and individual levels, enabling interpretable predictive inference. PMx approaches, when combined with more traditional statistical techniques, provide synergy for solving complex problems in modern drug development (Ryeznik et al., 2021).
Mechanistic PK/PD modeling to address early-stage biotherapeutic dosing feasibility questions
Published in mAbs, 2023
Joshuaine Grant, Fei Hua, Joshua F. Apgar, John M. Burke, Diana H. Marcantonio
While the use of mechanistic modeling is expanding, the application in the early stages of drug discovery and development is trailing behind the application in clinical stages. This is evident in a recent survey which shows that quantitative systems pharmacology models (including mechanistic PK/PD models) are more frequently applied to address questions on dose selection and clinical trial design than on candidate selection and target validation.50 Part of the reason for not applying modeling earlier in drug discovery may be the early discovery team’s lack of familiarity with mechanistic modeling compared to the clinical teams who have substantial experience with PK/PD or population PK modeling. In addition, since PK/PD models and population PK models are data-driven, the team may hesitate to use modeling in early discovery programs, as molecule-specific data are not yet available. The examples discussed in here serve to demonstrate that models based on literature information and target biology-related information can be very useful in making early predictions when applied appropriately.
Advances in omics for informed pharmaceutical research and development in the era of systems medicine
Published in Expert Opinion on Drug Discovery, 2018
Jane P. F. Bai, Ioannis N. Melas, Junguk Hur, Ellen Guo
Omics technologies are an integral part of informed pharmaceutical R&D, and their role in R&D will continue to expand. There are still challenges to be addressed to fully utilize omics technologies, especially for treating complex diseases, such as neurological and autoimmune diseases. Just to name a few. How to establish omics data standards to reduce inter-laboratory variability and to increase confidence in distinctly classifying disease subtypes to aid the design of clinical trials? How to translationally bridge omics data in pathological conditions and clinical phenotypes of individual diseases? How to integrate multiple layers of omics information and phenotypic characterizations, including pathological biomarkers of a disease, pharmacodynamics responses to a drug treatment? How to link omics data to brain images or cognitive scores for neurological or psychological diseases? Computational and statistical methods will undoubtedly continue to play a role. Looking ahead, quantitative systems pharmacology may help bridge the gap. It is anticipated that the molecular networks described by omics data and the physiological/pathological networks presented by clinical tests/diagnosis will be quantitatively integrated to inform R&D.
Proteomic characterisation of drug metabolising enzymes and drug transporters in pig liver
Published in Xenobiotica, 2020
Yasmine Elmorsi, Hajar Al Feteisi, Zubida M. Al-Majdoub, Jill Barber, Amin Rostami-Hodjegan, Brahim Achour
In conclusion, this study employed complementary sample preparation methods in conjunction with label-free proteomics to identify and quantify a large number of enzymes and transporters in pig liver. The putative identification undertaken in this study highlights the need for further annotation of existing mammalian databases. Abundance data, such as those reported in this study, can serve to populate translational quantitative systems pharmacology models used to predict drug pharmacology and toxicity in patients (Suenderhauf & Parrott, 2013). Limitations of the current study include the low sample size (2 livers) and the level of quantification achieved (relative quantification).