Dataset Information

Experimental training in molecular pharmacology education based on drug-target interactions.

ABSTRACT:

Introduction

Drug development has been challenged by the dual drawbacks involving unpredictable disease outcomes and drug resistance, which has placed greater demands on pharmacology education. Molecular pharmacology, as a frontier crossover field of pharmacology, focuses on the research of new drugs and targets. However, due to the lack of a systematic experimental training system, molecular pharmacology has not made a corresponding contribution in promoting the training of innovative talent in pharmacology. We aim to establish an experimental training program suitable for molecular pharmacology to improve students' ability to engage in drug development in future.

Methods

Based on the feasibility of drug-target projects, a comprehensive training program containing molecular docking, target stability experiment, and fluorescent probe detection of protein expression in living cells and mice was conducted among 20 pharmacy graduate students. The experimental training was assessed by the experimental training report and the student recognition questionnaires.

Results

All 20 students mastered the experimental principles and operations required for the training program. The experimental reports proved that the students were in good command of the experimental principles, operations and applications. The results of the Likert questionnaire indicated that the training program promoted the understanding of the drug research process and increased motivation to learn.

Conclusion

The designed experimental training program has a positive effect on the training of pharmacology talents, and can be implemented as a part of molecular pharmacology education.

SUBMITTER: Ge L

PROVIDER: S-EPMC10405239 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Publications

Experimental training in molecular pharmacology education based on drug-target interactions.

Ge Luna L Cheng Kai K Zhang Yuang Y Li Junling J Chen Wei W Song Guanhua G Wang Lin L

Pharmacology research & perspectives 20230801 4

<h4>Introduction</h4>Drug development has been challenged by the dual drawbacks involving unpredictable disease outcomes and drug resistance, which has placed greater demands on pharmacology education. Molecular pharmacology, as a frontier crossover field of pharmacology, focuses on the research of new drugs and targets. However, due to the lack of a systematic experimental training system, molecular pharmacology has not made a corresponding contribution in promoting the training of innovative t ...[more]

PMID: 37548279

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Project description:Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins' structure/function, and bias in system training datasets. Here, we propose a new method "DRUIDom" (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound-target pairs (~2.9M data points), and used as training data for calculating parameters of compound-domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound-protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound-domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.

Project description:More than 50 million adults in America suffer from chronic pain. Opioids are commonly prescribed for their effectiveness in relieving many types of pain. However, excessive prescribing of opioids can lead to abuse, addiction, and death. Non-steroidal anti-inflammatory drugs (NSAIDs), another major class of analgesic, also have many problematic side effects including headache, dizziness, vomiting, diarrhea, nausea, constipation, reduced appetite, and drowsiness. There is an urgent need for the understanding of molecular mechanisms that underlie drug abuse and addiction to aid in the design of new preventive or therapeutic agents for pain management. To facilitate pain related small-molecule signaling pathway studies and the prediction of potential therapeutic target(s) for the treatment of pain, we have constructed a comprehensive platform of a pain domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated data mining computing tools. Our new computing platform describes the chemical molecules, genes, proteins, and signaling pathways involved in pain regulation. Pain-CKB is implemented with a friendly user interface for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, and Spider Plot. Combining these with other novel tools, we performed three case studies to systematically demonstrate how further studies can be conducted based on the data generated from Pain-CKB and its algorithms and tools. First, systems pharmacology target mapping was carried out for four FDA approved analgesics in order to identify the known target and predict off-target interactions. Subsequently, the target mapping outcomes were applied to build physiologically based pharmacokinetic (PBPK) models for acetaminophen and fentanyl to explore the drug-drug interaction (DDI) between this pair of drugs. Finally, pharmaco-analytics was conducted to explore the detailed interaction pattern of acetaminophen reactive metabolite and its hepatotoxicity target, thioredoxin reductase.

Dataset Information

Experimental training in molecular pharmacology education based on drug-target interactions.

Introduction

Methods

Results

Conclusion

Publications

Experimental training in molecular pharmacology education based on drug-target interactions.

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