Accelerated drug discovery by rapid candidate drug identification

Bergström, Fredrik; Lindmark, Bo

doi:10.1016/j.drudis.2019.03.026

Cited by 23 publications

(15 citation statements)

References 29 publications

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“…There are several efforts like Chinese Medicine Integrated Database (TCMID) [183], Collective Molecular Activities of Useful Plants (CMAUP) [184], SymMap [185], encyclopedia of traditional Chinese medicine (ETCM) [186] etc. In addition, several researchers have developed strategies for in silico pharmacokinetic properties of molecules/drugs [187][188][189][190][191]. Such approaches are also applicable to phytochemicals and plant-based active drug components for their virtual screening, possible mode of action, and advanced drug discovery [192][193][194][195].…”

Section: Modern Trends In Traditional Medicine Informatics and Opportmentioning

confidence: 99%

Anticancer Plants: A Review of the Active Phytochemicals, Applications in Animal Models, and Regulatory Aspects

Khan

Ali

Khan³

et al. 2019

Biomolecules

205

111

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The rising burden of cancer worldwide calls for an alternative treatment solution. Herbal medicine provides a very feasible alternative to western medicine against cancer. This article reviews the selected plant species with active phytochemicals, the animal models used for these studies, and their regulatory aspects. This study is based on a meticulous literature review conducted through the search of relevant keywords in databases, Web of Science, Scopus, PubMed, and Google Scholar. Twenty plants were selected based on defined selection criteria for their potent anticancer compounds. The detailed analysis of the research studies revealed that plants play an indispensable role in fighting different cancers such as breast, stomach, oral, colon, lung, hepatic, cervical, and blood cancer cell lines. The in vitro studies showed cancer cell inhibition through DNA damage and activation of apoptosis-inducing enzymes by the secondary metabolites in the plant extracts. Studies that reported in vivo activities of these plants showed remarkable results in the inhibition of cancer in animal models. Further studies should be performed on exploring more plants, their active compounds, and the mechanism of anticancer actions for use as standard herbal medicine.

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Section: Modern Trends In Traditional Medicine Informatics and Opportmentioning

confidence: 99%

Anticancer Plants: A Review of the Active Phytochemicals, Applications in Animal Models, and Regulatory Aspects

Khan

Ali

Khan³

et al. 2019

Biomolecules

205

111

View full text Add to dashboard Cite

show abstract

“…The discovery of drug candidates capable of blocking or activating the desired target proteins involves extensive virtual and experimental screening that accounts for 30–40% of the total time invested in drug development [ 1 ]. Given the difficulty of directly finding an optimal drug candidate with desirable therapeutic potency, and absorption, distribution, metabolism and elimination (ADME) and toxicity profile [ 2 ], the success of a drug discovery campaign is strongly affected by the efficiency of lead optimization.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion

Yang

et al. 2021

J Cheminform

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In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization tasks. Especially, the integration of MMPA with QSAR modeling can further strengthen the utility of MMPA in molecular optimization navigation. In this study, a new semi-automated procedure based on KNIME was developed to support MMPA on both large- and small-scale datasets, including molecular preparation, QSAR model construction, applicability domain evaluation, and MMP calculation and application. Two examples covering regression and classification tasks were provided to gain a better understanding of the importance of MMPA, which has also shown the reliability and utility of this MMPA-by-QSAR pipeline.

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“… 1 , 2 This is largely attributable to the tendency to extract metabolically stable compounds during high‐throughput absorption, distribution, metabolism, and excretion (ADME) screening to achieve adequate exposure at a lower dosage in humans. 3 In other words, as a result of the selection of low intrinsic CL (CL int ) compounds, it has become increasingly common that IVIVE cannot be applied because of the low turnover of the parent compound in microsomal or hepatocyte stability assays. In these in vitro assays, it is difficult to estimate the in vitro CL int of low‐CL int compounds because of the limited incubation time for which enzymatic activity can be maintained.…”

Section: Introductionmentioning

confidence: 99%

Prediction of human pharmacokinetics for low‐clearance compounds using pharmacokinetic data from chimeric mice with humanized livers

Yoshida

Doi²,

Iwazaki³

et al. 2021

Clinical Translational Sci

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Development of low-clearance (CL) compounds that can be slowly metabolized is a major goal in the pharmaceutical industry. However, the pursuit of low intrinsic CL (CL int ) often leads to significant challenges in evaluating the pharmacokinetics of such compounds. Although in vitro-in vivo extrapolation is widely used to predict human CL, its application has been limited for low-CL int compounds because of the low turnover of parent compounds in metabolic stability assays. To address this issue, we focused on chimeric mice with humanized livers (PXB-mice), which have been increasingly reported to accurately predict human CL in recent years. The predictive accuracy for nine low-CL int compounds with no significant turnover in a human hepatocyte assay was investigated using PXB-mouse methods such as single-species allometric scaling (PXB-SSS) approach and a novel physiologically based scaling (PXB-PBS) approach that assumes that the CL int per hepatocyte is equal between humans and PXB-mice. The percentages of compounds with predicted CL within 2-and 3-fold ranges of the observed CL for low-CL int compounds were 89% and 100%, respectively, for both PXB-SSS and PXB-PBS approaches. Moreover, the predicted CL was mostly consistent among the methods. Conversely, percentages of compounds with predicted CL within 2-and 3-fold ranges of the observed CL for low-CL int compounds were 50% and 63%, respectively for multispecies allometric scaling (MA). Overall, these PXB-mouse methods were much more accurate than conventional MA approaches, suggesting that PXB-mice are useful tool for predicting the human CL of low-CL int compounds that are slowly metabolized.

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Accelerated drug discovery by rapid candidate drug identification

Cited by 23 publications

References 29 publications

Anticancer Plants: A Review of the Active Phytochemicals, Applications in Animal Models, and Regulatory Aspects

Anticancer Plants: A Review of the Active Phytochemicals, Applications in Animal Models, and Regulatory Aspects

Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion

Prediction of human pharmacokinetics for low‐clearance compounds using pharmacokinetic data from chimeric mice with humanized livers

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