Novel Dual-Stage Antimalarials: Machine learning prediction, validation and evolution

新型双阶段抗疟药：机器学习预测、验证和进化

基本信息

批准号：
10742205
负责人：
Emily R Derbyshire
金额：
$ 24.64万
依托单位：
RUTGERS BIOMEDICAL AND HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-03 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10742205
关键词：
Address Animal Model Antimalarials Artemisinins Biological Biological Assay Biology Blood Blood Cells Cells Cessation of life Chemical Structure Chloroquine Chloroquine resistance Competence Computer Models Data Development Disease Diversity Library Drug Kinetics Drug resistance Effectiveness Erythrocytes Evaluation Evolution Future Goals Grant Growth HepG2 Hepatocyte In Vitro Infection Invaded Lead Libraries Life Life Cycle Stages Liver Liver Microsomes Machine Learning Malaria Malignant Neoplasms Measures Modeling Molecular Mus Organic Synthesis Parasite resistance Parasites Pharmaceutical Chemistry Pharmaceutical Preparations Pharmacology Physicians Plasmodium Plasmodium falciparum Probability Productivity Property Prophylactic treatment Publications Pyrimethamine Reporting Research Personnel Resistance Resources Risk Solubility Sulfadoxine Symptoms Technology Testing Therapeutic Time Training Triage Validation analog aqueous candidate identification cost cost efficient cytotoxicity design disorder control drug development drug discovery efficacy evaluation experience heuristics high throughput screening human disease in vivo inhibitor innovation machine learning method machine learning model machine learning prediction malaria infection meetings new therapeutic target novel novel strategies novel therapeutic intervention novel therapeutics pathogen prevent prophylactic random forest screening skills small molecule statistics tool transmission process

项目摘要

PROJECT SUMMARY Specifically, this proposal focuses on novel new small molecules that inhibit both the blood and liver stages of malaria infection. The causative pathogen – Plasmodium spp. – was responsible for 241,000,000 cases that resulted in 627,000 deaths in 2020. Plasmodium spp. drug-resistant infections leave few good choices for physicians and put at risk the productivity and the lives of those infected. A clear case has been made for new drugs to treat these infections through the discovery and development of novel therapeutic strategies. These strategies would optimally be dual stage, targeting the blood stage for treatment and the liver stage for prophylaxis. The innovative strategy in this proposal builds on the technology of machine learning models for the prediction of novel dual-stage antimalarial small molecules with significant potential as drug discovery entities. Such a computational approach to seed the discovery of small molecule malaria parasite inhibitors with dual- stage efficacy has only been reported by us in 2022. The approach begins with preliminary data around two novel antimalarial small molecules with demonstrated in vitro efficacy versus both blood and liver stages of Plasmodium spp. infection and a lack of significant cytotoxicity to cultured liver cells. These molecules were derived from a set of hits discovered with a random forest model trained with high-throughput screening data. The molecules are representative of novel chemotypes for dual-stage antimalarials and, thus, offer a high probability of modulating new targets that are critical throughout the parasite’s lifecycle. This initial machine learning effort will be significantly expanded with a range of model types and a different and larger commercial library to predict a set of new hit compounds. Two validated hits, meeting in vitro efficacy and cytotoxicity criteria and maintaining wild type in vitro efficacy versus a set of drug-resistant parasite strains, will be profiled for key molecular properties such as mouse liver microsomal stability, aqueous solubility, and mouse pharmacokinetic profile. These data along with the existing in vitro efficacy and cytotoxicity evaluations will guide the evolution of each hit with a goal of preparing one or more analogs with a composite profile to enable downstream in vivo efficacy evaluation in infection models. A novel combination of medicinal chemistry and machine learning will be leveraged to afford such molecules.

项目概要具体来说，该提案重点关注抑制血液和肝脏的新型小分子疟疾感染的病原体——疟原虫——导致了 241,000,000 例病例。 2020 年，导致 627,000 人死亡。疟原虫感染几乎没有什么好的选择。医生的工作效率和感染者的生命都处于危险之中，这是一个明显的案例。通过药物发现和开发新的治疗策略来治疗这些感染。最佳策略是双阶段，针对血液阶段进行治疗，针对肝脏阶段进行治疗预防。该提案中的创新策略建立在机器学习模型技术的基础上预测具有作为药物发现实体的巨大潜力的新型双阶段抗疟小分子。这种计算方法为发现具有双重功能的小分子疟疾寄生虫抑制剂奠定了基础我们直到 2022 年才报告了阶段功效。该方法从两部小说的初步数据开始抗疟小分子，对疟原虫的血液阶段和肝脏阶段均具有体外功效 spp.感染并且对培养细胞缺乏显着的肝细胞毒性。使用高通量筛选数据训练的随机森林模型发现的一组命中结果。是双阶段抗疟药新化学型的代表，因此，提供了很高的可能性调节在寄生虫整个生命周期中至关重要的新目标。通过一系列模型类型和不同的、更大的商业库来显着扩展，以预测一组新的热门化合物。两个经过验证的命中，满足体外功效和细胞毒性标准并在体外维持野生型与一组耐药寄生虫菌株相比的功效，将针对关键分子特性进行分析，例如小鼠肝微粒体稳定性、水溶性和小鼠药代动力学特征。现有的体外功效和细胞毒性评估将指导每个命中的进化，目标是准备具有复合特征的一种或多种类似物，以实现感染中的下游体内功效评估将利用药物化学和机器学习的新颖组合来提供此类模型。分子。