In silico models in drug development: where we are

Piñero, Janet; Furlong, Laura I.; Sanz, Ferrán

doi:10.1016/j.coph.2018.08.007

Cited by 40 publications

(23 citation statements)

References 117 publications

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“…The added value of in silico models for drug development is now unanimously recognized by the scientific community 1,2 . Irrespective of the model used and the concerned part of the drug development pipeline, the evidence generated from these models, also called digital evidence , might eventually be included in regulatory submissions.…”

Section: Current Status Gaps and Challenges In Assessment Of Modelsmentioning

confidence: 99%

Verifying and Validating Quantitative Systems Pharmacology and In Silico Models in Drug Development: Current Needs, Gaps, and Challenges

Musuamba

Bursi²,

Manolis

et al. 2020

CPT Pharmacom & Syst Pharma

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The added value of in silico models (including quantitative systems pharmacology models) for drug development is now unanimously recognized. It is, therefore, important that the standards used are commonly acknowledged by all the parties involved. On April 25 and 26, 2019, a multistakeholder workshop on the validation challenges for in silico models in drug development was organized in Belgium. As an outcome, a White Paper is foreseen in 2020 on standards for in silico model verification and validation. CURRENT STATUS, GAPS, AND CHALLENGES IN ASSESSMENT OF MODELS FOR REGULATORY SUBMISSIONSDrug research, design, and development has a long-standing tradition in the use of in silico methodologies. In the context of clinical drug development Quantitative Structure-Property Relationship models in general and Quantitative Structure-Activity Relationship (QSAR) methods in particular, as well as pharmacometric approaches like population pharmacokinetics, pharmacokinetics (PKs)/pharmacodynamics, exposure-response, and physiology-based pharmacokinetics (PBPK) models are well-known. However, the in silico toolbox is rapidly expanding beyond these traditional/historical modeling technologies and new ones have emerged the last decades, including multiphysics simulations, the so-called systems medicine/pharmacology models (QSP) and clinical trial simulation tools (in silico clinical trials). In the remainder of this document, the term in silico models will be used to describe the collection of all the aforementioned modeling technologies.The added value of in silico models for drug development is now unanimously recognized by the scientific community. 1,2 Irrespective of the model used and the concerned part of the drug development pipeline, the evidence generated from these models, also called digital evidence, might eventually be included in regulatory submissions. In that case, the incorporation of digital evidence needs to follow standards of data/evidence generation, analysis, and reporting to enable the regulatory bodies to efficiently perform an adequate assessment of the submitted material.It is, therefore, of utmost importance that the standards to be considered are commonly acknowledged by all the involved parties (regulators, health technology assessment (HTA) agencies, academia, industry, regulators, and patients) and are relevant for all the types of models that can be included in regulatory submissions. The endorsement of these standards by regulators is particularly valuable because regulators generally provide guidance for data generation and reporting back to sponsors (industry or academia) thereby accelerating the uptake of the standards in the entire community and in the healthcare systems.Guidance documents have been published for QSAR models, 3 population PK models, 4 PK/pharmacodynamic or exposure-response models, 5,6 and more recently PBPK models, both by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA). 7,8 However, these guidelines are not fully applicable to all emergi...

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Section: Current Status Gaps and Challenges In Assessment Of Modelsmentioning

confidence: 99%

Verifying and Validating Quantitative Systems Pharmacology and In Silico Models in Drug Development: Current Needs, Gaps, and Challenges

Musuamba

Bursi²,

Manolis

et al. 2020

CPT Pharmacom & Syst Pharma

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“…Seven drugs were identified using this model, three of which are supported by literature findings and three are experimentally validated by cytotoxicity assays using cell lines (118). Moreover, in silico models are used for toxicity assessments on the level of liver, gastrointestinal tract, and blood-brain barrier (119,120). Such computational models aim to understand the side effects of drug candidates from molecular changes to phenotypic manifestations.…”

Section: In Silico Modelsmentioning

confidence: 79%

Upcoming Revolutionary Paths in Preclinical Modeling of Pancreatic Adenocarcinoma

2020

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“…In-silico modeling of human pharmacokinetics, for example, is incorporated into drug development pipelines by a majority of pharmaceutical companies [74]. Similarly, the insilico prediction of drug toxicity in human tissues has benefited from recent advances in the field of quantitative systems toxicology (QST) by incorporating techniques including gene set enrichment analysis, gene signature analysis, and co-expression network analysis [75]. These advances herald new opportunities in drug design that enable consortia like transQST iv to incorporate existing large datasets like transcriptomics, proteomics and metabolomics to predict drug toxicity in humans at the preclinical stage.…”

Section: Predicting and Identifying Pharmacokinetic Changes By The MImentioning

confidence: 99%

Predicting and Understanding the Human Microbiome’s Impact on Pharmacology

Hitchings

Kelly

2019

Trends in Pharmacological Sciences

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Our bodies each possess a unique and dynamic collection of microbes and viruses, collectively the "microbiome", with distinct metabolic capacities from our human cells. Unforeseen modification of drugs by the microbiome can drastically alter clinical effectiveness, in the most dramatic cases leading to fatal drug interactions. Pharmaceuticals can be activated, deactivated, toxified, or release metabolites that alter the "canonical" pharmacokinetics of the drug. Predicting and characterizing microbe-drug interactions is thus necessary to develop and implement personalized drug administration protocols and, more broadly, to improve drug safety and efficacy. This review focuses on microbiome driven alterations to drug pharmacokinetics and provides a research framework for pharmacologists interested in characterizing microbiome interactions with any drug of interest.

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In silico models in drug development: where we are

Cited by 40 publications

References 117 publications

Verifying and Validating Quantitative Systems Pharmacology and In Silico Models in Drug Development: Current Needs, Gaps, and Challenges

Verifying and Validating Quantitative Systems Pharmacology and In Silico Models in Drug Development: Current Needs, Gaps, and Challenges

Upcoming Revolutionary Paths in Preclinical Modeling of Pancreatic Adenocarcinoma

Predicting and Understanding the Human Microbiome’s Impact on Pharmacology

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