In Silico Study and Optimization of Molecular Nanomotors for Membrane Photopharmacology

膜光药理学分子纳米马达的计算机研究和优化

• 批准号: 10629113
• 负责人: Enrico Tapavicza
• 金额: $ 14.75万
• 依托单位: CALIFORNIA STATE UNIVERSITY LONG BEACH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10629113
• 关键词: Address; Adverse effects; Affinity; Antibiotic Therapy; Bacterial Antibiotic Resistance; Binding; Biological; Cell Death Induction; Cell membrane; Cells; Clinical Research; Collaborations; Communicable Diseases; Computer Models; Computer Simulation; Computer software; Computing Methodologies; Development; Drug Controls; Drug Delivery Systems; Drug resistance; Drug usage; Employment; Gases; Generations; Goals; Isomerism; Light; Location; Machine Learning; Malignant neoplasm of prostate; Measures; Mechanics; Mediating; Membrane; Membrane Lipids; Methods; Modality; Modernization; Modification; Molecular; Molecular Structure; Motor; Nature; Nuclear; Operative Surgical Procedures; Patients; Penetration; Perforation; Pharmaceutical Preparations; Pharmacotherapy; Phase; Phospholipids; Photons; Phototoxicity; Process; Property; Quantum Mechanics; Radiation; Radiation induced damage; Rotation; Structure; System; Techniques; Technology; Testing; Therapeutic; Time; Tissues; Toxic effect; Training; Ultraviolet Rays; Work; absorption; cancer cell; cancer therapy; chemotherapy; clinical application; design; drug development; experimental study; functional group; high throughput screening; improved; in silico; insight; irradiation; light intensity; lipid nanoparticle; machine learning algorithm; mechanical properties; molecular dynamics; molecular mechanics; nano; next generation; novel strategies; pi bond; quantum; side effect; simulation; two-photon; ultraviolet

Project Summary/Abstract There is a dire need in developing new molecular paradigms for pharmacotherapy to address problems of poor drug selectivity, causing side effects, drug resistance, and environmental toxicity. Currently, over 85 % of the drugs in clinical research are discarded due to poor selectivity. Increasing drug selectivity is a major concern of modern drug development. Photopharmacology increases drug selectivity by using controlled light-activation of drugs at a given time and location in the body. Recently, a light-driven molecular nanomotor has been developed (García- López et al., Nature, 548, 7669, 567, 2017), that is capable of disrupting biological membranes, inducing cell death in eucaryotic cells. This mechanism has potential applications in drug delivery through lipid nanoparticles, cancer treatment, and combating infectious diseases. Besides chemotherapy, radiation, and surgery, mechanical action of nanomotors on a molecular level could become a fourth modality in the treatment of patients. However, being still at a developmental stage, a detailed understanding of the molecular mechanism of this process is required to advance this technology towards clinical applications. We will use computer modeling to study the molecular mechanism of the membrane disruption by the recently developed nanomotor. Based on the gained insight, we will design and optimize new nanomotors by introducing functional groups to improve molecular prop- erties. The final goal is to develop a next generation of nanomotors that can be applied in clinical studies. To reduce phototoxicity, it is necessary that the motor operates with a high quantum yield, converting a high percent- age of the absorbed photons into mechanical work to displace membrane lipids. Furthermore, tissue applications require the irradiation wavelength to occur in the 600–1000 nm region, which penetrates deeper than the initially used ultraviolet light, that also has higher phototoxicity. We will employ computational methods based on quantum mechanics, molecular mechanics, and machine learning. Core of our study will be the real time simulation of the photoinduced dynamics of the nanomotor in the membrane, yielding atomistic information about the membrane disruption process. To this end we will use machine learning driven molecular dynamics. The machine learning algorithm will be trained using quantum mechanical simulations. Based on the gained insights, several molecular properties will be enhanced by modifications of the functional groups: a) binding affinity to the membrane; b) light absorption in the near infrared or visible region; c) absorption cross section and quantum yield. To obtain candidate molecules we will employ in silico high-throughput screening based on exhaustive molecule generation and machine learning of quantum mechanical properties. Candidates with improved properties will be synthe- sized by the García-López lab and studied experimentally to gauge the validity of the predictions. The results of this combined computational/experimental study will give a detailed atomistic picture of the dynamics of the nanomotors membranes. The proposed molecular modifications will lead to a next generation of nanomotors, opening this technology for clinical studies, leading to highly selective light-activated drugs.

项目总结/摘要 迫切需要开发新的药物治疗分子范例，以解决穷人的问题。 药物选择性，引起副作用，耐药性和环境毒性。目前，超过85%的药物 在临床研究中，由于选择性差而被丢弃。增加药物选择性是现代药物治疗的主要关注点。 药物开发光药理学通过使用药物的受控光活化来增加药物的选择性， 在身体中的特定时间和位置。最近，已经开发了一种光驱动的分子纳米粒子（García- López等人，Nature，548，7669，567，2017），其能够破坏生物膜，诱导细胞凋亡， 真核细胞死亡。这一机制在脂质纳米粒的药物传递中具有潜在的应用， 癌症治疗和抗击传染病。除了化疗、放疗和手术， 纳米马达在分子水平上的作用可能成为治疗患者的第四种方式。然而，在这方面， 由于仍处于发展阶段，因此对这一过程的分子机制的详细了解是 需要将这项技术推向临床应用。我们将使用计算机建模来研究 膜破裂的分子机制，最近开发的纳米粒子。根据获得的 洞察力，我们将设计和优化新的纳米电机通过引入官能团，以提高分子特性， 额。最终目标是开发可应用于临床研究的下一代纳米电机。到 减少光毒性，有必要使马达以高量子产率运行，将高百分比的 吸收的光子转化为机械功以取代膜脂质。此外，组织应用 需要照射波长在600-1000 nm区域内，其穿透比初始波长更深。 使用紫外线，也具有较高的光毒性。我们将采用基于量子力学的计算方法， 分子力学和机器学习我们研究的核心将是真实的时间模拟 膜中纳米粒子的光致动力学，产生关于膜的原子信息 中断过程。为此，我们将使用机器学习驱动的分子动力学。机器学习 将使用量子力学模拟来训练算法。根据所获得的见解，一些分子 性质将通过官能团的改性来增强：a）与膜的结合亲和力; B） 在近红外或可见光区的光吸收; c）吸收截面和量子产率。获得 候选分子，我们将采用基于穷举分子生成的计算机高通量筛选 和量子力学性质的机器学习。具有改进性质的候选人将合成- 由加西亚-洛佩斯实验室调整大小，并进行实验研究，以衡量预测的有效性。结果 这种结合计算/实验研究将给出一个详细的原子动力学的图片， 纳米马达膜。所提出的分子修饰将导致下一代纳米马达， 将这项技术用于临床研究，从而产生高选择性的光激活药物。