In Silico Pharmacology for Evidence-Based and Precision Medicine, 2nd Edition

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Clinical Pharmaceutics".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 2823

Special Issue Editor


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Guest Editor
1. Department of Forensic Sciences and Toxicology, Faculty of Medicine, University of Crete, Heraklion, Greece
2. Computational BioMedicine Lab, Institute of Computer Science, Foundation for Research & Technology - Hellas, Heraklion, Greece
Interests: pharmacokinetics & pharmacodynamics; PBPK modeling and simulation; drug interactions; computational medicine; in silico pharmacology; pharmacometrics; computational oncology
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Special Issue Information

Dear Colleagues,

Personalized/precision medicine (PM), initiating from clinical and molecular pharmacology, introduces a new era in healthcare that aims to identify and predict the optimum treatments for a patient or a cohort. Evidence-based medicine (EBM) integrates information from basic in vivo, observational studies and clinical trials up to meta-analysis data for clinical consideration. Hence, it can be stated that EBM often see the forest (population averages) but misses the trees (individual patients), whereas the utilization of PM may not see the forest for the trees.

State-of-the-art tools for modeling and simulation (M&S) in pharmacology try to extrapolate knowledge gained through experimental procedures with either top-down or bottom-up approaches. M&S often try to “connect the dots” and reveal the bigger picture, the “population/forest”, considering what kind of “individuals/trees” are in there. These approaches have been providing state-of-the-art tools in research and development (R&D) for novel, more efficient and effective molecules, with improved safety profiles and the chance to proceed in clinical trials. In addition, they contribute to the R&D of novel drug-delivery systems and finally assist regarding drug repurposing.

This Special Issue invites research and review articles on pharmacological approaches incorporating M&S to promote EBM and PM knowledge for potential clinical extrapolation. Articles utilizing experimental or clinical data through M&S biomedical tools for disease progression dynamics, machine learning and relevant bioengineering approaches for drug response prediction (including drug interactions, adverse drug reactions and drug repurposing), PK/PD models for special population groups or novel drug delivery systems extrapolating in vitro/in vivo data to the clinical level are some of the (but not the only) research areas that this Special Issue aims to include.

Dr. Marios Spanakis
Guest Editor

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Keywords

  • personalized/precision medicine
  • evidence-based medicine approaches for therapy optimization
  • physiologically based PK/PD models
  • drug targeting and response prediction
  • multiscale M&S for disease dynamics
  • population pharmacokinetics/pharmacodynamics
  • in silico clinical trials
  • big data and systems pharmacology
  • M&S in clinical settings
  • translational and biomedical informatics in precision medicine

Published Papers (2 papers)

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Research

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14 pages, 1902 KiB  
Article
Retinoic Acid Receptor Is a Novel Therapeutic Target for Postoperative Cognitive Dysfunction
by Yongjie Bao, Wenni Rong, An Zhu, Yuan Chen, Huiyue Chen, Yirui Hong, Jingyang Le, Qiyao Wang, C. Benjamin Naman, Zhipeng Xu, Lin Liu, Wei Cui and Xiang Wu
Pharmaceutics 2023, 15(9), 2311; https://doi.org/10.3390/pharmaceutics15092311 - 13 Sep 2023
Cited by 2 | Viewed by 954
Abstract
Postoperative cognitive dysfunction (POCD) is a clinical syndrome characterizing by cognitive impairments in the elderly after surgery. There is limited effective treatment available or clear pathological mechanisms known for this syndrome. In this study, a Connectivity Map (CMap) bioinformatics model of POCD was [...] Read more.
Postoperative cognitive dysfunction (POCD) is a clinical syndrome characterizing by cognitive impairments in the elderly after surgery. There is limited effective treatment available or clear pathological mechanisms known for this syndrome. In this study, a Connectivity Map (CMap) bioinformatics model of POCD was established by using differently expressed landmark genes in the serum samples of POCD and non-POCD patients from the only human transcriptome study. The predictability and reliability of this model were further supported by the positive CMap scores of known POCD inducers and the negative CMap scores of anti-POCD drug candidates. Most retinoic acid receptor (RAR) agonists were negatively associated with POCD in this CMap model, suggesting that RAR might be a novel target for POCD. Most importantly, acitretin, a clinically used RAR agonist, significantly inhibited surgery-induced cognitive impairments and prevented the reduction in RARα and RARα-target genes in the hippocampal regions of aged mice. The study denotes a reliable CMap bioinformatics model of POCD for future use and establishes that RAR is a novel therapeutic target for treating this clinical syndrome. Full article
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25 pages, 1916 KiB  
Review
Rewiring Drug Research and Development through Human Data-Driven Discovery (HD3)
by David B. Jackson, Rebecca Racz, Sarah Kim, Stephan Brock and Keith Burkhart
Pharmaceutics 2023, 15(6), 1673; https://doi.org/10.3390/pharmaceutics15061673 - 07 Jun 2023
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Abstract
In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation [...] Read more.
In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research. Full article
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