In silico ADME in drug design – enhancing the impact

Winiwarter, Susanne; Ahlberg, Ernst; Watson, Edmund; Oprisiu, Ioana; Mogemark, Mickael; Noeske, Tobias; Greene, Nigel

doi:10.5599/admet.6.1.470

Cited by 16 publications

(8 citation statements)

References 32 publications

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“…The value of the panel data for the optimization of internal prediction models has already been described, 47 tools that are continuously applied for characterization of virtual compound sets and associated decision-making for compound synthesis. Here, we additionally illustrate the role of panel assay results in the optimization of inhibitors of the Ataxia Telangiectasia Mutated (ATM) kinase ( Fig.…”

Section: Resultsmentioning

confidence: 99%

A Fully Integrated Assay Panel for Early Drug Metabolism and Pharmacokinetics Profiling

Wernevik

Bergström

Novén

et al. 2020

ASSAY and Drug Development Technologies

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Evaluation and optimization of physicochemical and metabolic properties of compounds are a crucial component of the drug development process. Continuous access to this information during the design-make-test-analysis cycle enables identification of chemical entities with suitable properties for efficient project progression. In this study, we describe an integrated and automated assay panel (DMPK Wave 1) that informs weekly on lipophilicity, solubility, human plasma protein binding, and metabolic stability in rat hepatocytes and human liver microsomes. All assays are running in 96-well format with ultraperformance liquid chromatography–mass spectrometry (MS)/MS as read-out. A streamlined overall workflow has been developed by optimizing all parts of the process, including shipping of compounds between sites, use of fit-for-purpose equipment and information systems, and technology for compound requesting, data analysis, and reporting. As a result, lead times can be achieved that well match project demands across sites independently of where compounds are synthesized. This robust screening strategy is run on a weekly basis and enables optimization of structure-activity relationships in parallel with DMPK properties to allow efficient and informed decision making.

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Section: Resultsmentioning

confidence: 99%

A Fully Integrated Assay Panel for Early Drug Metabolism and Pharmacokinetics Profiling

Wernevik

Bergström

Novén

et al. 2020

ASSAY and Drug Development Technologies

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“…The impact of these computational models on drug discovery is undeniable, evidenced by the successful prediction of biological activity and pharmacokinetic parameters, viz. absorption, distribution, metabolism, excretion, and toxicity (ADMET) [17][18][19][20][21]. For ligand-based QSAR/QSPR modeling, the structural features of molecules (e. g. as pharmacophore distribution, physicochemical properties, and functional groups) are commonly converted into machine-readable numbers using the so-called molecular descriptors [7].…”

Section: Qsar/qspr and Structure-based Modeling With Artificial Intelligencementioning

confidence: 99%

Artificial intelligence in drug discovery: recent advances and future perspectives

Jiménez-Luna

Grisoni

Weskamp

et al. 2021

Expert Opinion on Drug Discovery

247

122

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Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry. Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery. Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions.Open data sharing and model development will play a central role in the advancement of drug discovery with AI.

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“…absorption in the body, distribution into the different compartments, metabolism by organs, and elimination through the body [56]. It was necessary to perform a computational study to predict the ADME parameters of the designed molecules to prioritize the molecules for synthesis [57]. Hence ADME study is an essential step for checking the drug-likeness.…”

Section: In Silico Admet Predictionmentioning

confidence: 99%

A facile in silico drug design strategy based on reference listed drugs and computational modeling of novel anticancer therapeutics

More¹,

Thomas²,

Chitlange³

et al. 2019

Sanat

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Heterocyclic compounds present unique structural and physicochemical diversity. Especially, the aromatic heterocycliccompounds like indole exhibit anti-cancer properties by facilitating cancerous cell death. Drug discovery and development process relyto a large extent on the in silico identification of the putative targets and rational design of potentially therapeutic ligands. We propose a facile strategy of in silico drug designing hereby curating libraries having a functional group modification of the indole heterocyclic compounds with 2-chloro-N-(2-chloroethyl)-N-methylethanamine. Subsequently, we compare the designed drugs with the existing alkylating Reference Listed Drugs (RLDs) of the USFDA. We computationally model the indole ring as a basic scaffold and induce 2-chloro-N-(2chloroethyl)-N-methylethanamine substitution on the C-3 of indole with experimentally available target DNA receptor to design an extensive library of 200 molecules. This was followed by extensive ligand-DNA docking studies to predict putative targets andADMET prediction of an optimized ligand. Our simple in silico strategy reveals that the designed compounds such as AGSPBM134, AGSPBM133, AGSPBM131, AGSPBM130, AGSPBM132 and AGSPBM019 exhibitstructural similarity towards the RLD as shown by ECFP-6 fingerprints. We show that they pass all the similarity criteria of the physicochemical parameters with no violation of the Lipinski's rule of five. We positthat these agents can potentially be a good choice for further synthesis in the development of novel anti-cancer agents.

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In silico ADME in drug design – enhancing the impact

Abstract: Each year the pharmaceutical industry makes thousands of compounds, many of which do not meet the

Cited by 16 publications

References 32 publications

A Fully Integrated Assay Panel for Early Drug Metabolism and Pharmacokinetics Profiling

A Fully Integrated Assay Panel for Early Drug Metabolism and Pharmacokinetics Profiling

Artificial intelligence in drug discovery: recent advances and future perspectives

A facile in silico drug design strategy based on reference listed drugs and computational modeling of novel anticancer therapeutics

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