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

• 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
• 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

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.

项目摘要 具体而言，该提案的重点是抑制血液和肝脏的新型小分子 疟疾感染的阶段。病原体 - 疟原虫属。 - 负责241,000,000个案件 这导致2020年的627,000人死亡。疟原虫属。抗药性感染几乎没有选择 医师并冒险冒着感染者的生产力和生命。新的案件已经为新的 通过发现和发展新型治疗策略来治疗这些感染的药物。 策略将是双重阶段的最佳阶段，针对血液阶段进行治疗和肝脏阶段 预防。 该提案中的创新策略建立在机器学习模型的技术基础上 新型的双阶段抗疟疾小分子的预测，具有巨大潜力作为药物发现实体。 这种计算方法是用双重分子的寄生虫​​抑制剂发现小分子疟疾抑制剂 我们仅在2022年才报告了阶段效率。该方法始于两个新颖的初步数据 具有体外效率的抗疟疾小分子与疟原虫的血液和肝脏阶段 spp。感染和缺乏对培养的肝细胞的细胞毒性。这些分子源自 用随机森林模型发现的一组命中，该模型训练有高通量筛选数据。分子 代表了双阶段抗疟药的新型化学型，因此提供了很高的可能性 调节整个寄生虫生命周期至关重要的新目标。最初的机器学习工作将 通过一系列模型类型和一个不同的更大的商业库进行显着扩展，以预测 一组新的命中化合物。 两个经过验证的命中，满足体外效率和细胞毒性标准并在体外保持野生型 功效与一组抗药性寄生虫菌株将用于关键分子特性，例如小鼠 肝微粒体稳定性，水溶性和小鼠药代动力学特征。这些数据以及 现有的体外效率和细胞毒性评估将指导每个命中的演变，以准备 一个或多个具有复合曲线的类似物，可以在感染中进行下游体内效率评估 型号。药物化学和机器学习的一种新型组合将被利用为这样 分子。