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.

项目摘要/摘要 迫切需要开发新的药物治疗的分子范例来解决穷人的问题 药物选择性、引起的副作用、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。提高药物选择性是现代社会关注的一个主要问题 药物开发。光药理学通过使用受控的光激活药物来提高药物的选择性 身体中给定的时间和位置。最近，一种光驱动的分子纳米马达已经被开发出来(加西亚- L等，自然，548,7669,567,2017)，它能够破坏生物膜，诱导细胞 真核细胞中的死亡。这种机制在通过脂质纳米粒给药方面具有潜在的应用， 癌症治疗，与传染病作斗争。除了化疗，放射治疗和手术，机械的 纳米运动器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， 由于仍处于发展阶段，对这一过程的分子机制的详细了解是 将这项技术推向临床应用所需的。我们将使用计算机建模来研究 新近发展起来的纳米马达破坏膜的分子机制。基于所获得的 洞察，我们将通过引入官能团来改善分子支撑性，设计和优化新型纳米电机。 十字军装。fiNAL的目标是开发下一代可以应用于临床研究的纳米电机。至 为了减少光毒性，电机必须以高量子产率运行，将高百分比- 吸收的光子进入机械功以取代膜脂的年龄。此外，组织应用 要求辐射波长出现在600-1000 nm区域，该区域比最初的穿透更深 使用紫外光时，也具有较高的光毒性。我们将采用基于量子的计算方法 力学、分子力学和机器学习。我们研究的核心将是实时仿真 膜中纳米马达的光诱导动力学，产生关于膜的原子信息 颠覆过程。为此，我们将使用机器学习驱动的分子动力学。机器学习 算法将使用量子力学模拟进行训练。根据所获得的见解，几个分子 性能将通过官能团的Modifi阳离子来增强：a)将fi刚性结合到膜上；b) 近红外或可见光吸收；c)吸收截面和量子产额。为了获得 我们将在基于穷举分子生成的电子高通量筛选中使用候选分子 以及量子力学性质的机器学习。性能得到改善的候选人将被合成- 由García-López实验室测量，并进行实验研究，以衡量预测的有效性。结果是 这项计算和实验相结合的研究将给出一幅详细的原子学图景 纳米电动机膜。提出的分子Modifi阳离子将导致下一代纳米电机， 开放这项技术用于临床研究，导致高度选择性的光激活药物。