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

基本信息

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
项目类别：
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

项目摘要

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/摘要开发新的分子范式来解决药物疗法以解决贫困问题有迫切需要药物选择性，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的主要关注点药物开发。光电学通过使用受控的药物在给定的时间和位置。最近，已经开发了一个轻驱动的分子纳米运动（García- López等人，自然，548，7669，567，2017），能够破坏生物膜，诱导细胞桉树细胞死亡。该机制通过脂质纳米颗粒在药物递送中有潜在的应用，癌症治疗和打击传染病。除了化学疗法，放射线和手术外，机械纳米运动在分子水平上的作用可能会成为患者治疗的第四个模态。然而，仍处于发展阶段，对这一过程的分子机制的详细理解是将这项技术推向临床应用所需。我们将使用计算机建模来研究最近开发的纳米运动膜破坏膜的分子机制。基于获得的洞察力，我们将通过引入官能团来设计和优化新的纳米运动，以改善分子支持 Erties。最后的目标是开发可用于临床研究中的下一代纳米运动。到降低光毒性，有必要以高量子产率运行，将高百分比转换吸收照片的年龄将膜脂质置换为机械工作。此外，组织应用需要在600–1000 nm区域中发生辐照波长，该区域渗透比最初更深使用的紫外线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对膜中纳米运动的照片诱导的动力学，得出有关膜的原子信息破坏过程。为此，我们将使用机器学习驱动器分子动力学。机器学习算法将使用量子机械模拟训练。基于获得的见解，几个分子函数组的修改将增强属性：a）与膜的结合； b）近红外或可见区域的遗憾； c）遗憾横截面和量子收率。获得我们将基于详尽的分子生成的硅高通量筛选中使用的候选分子和量子机械性能的机器学习。具有改善特性的候选者将是合成的由García-López实验室进行的，并通过实验研究以评估预测的有效性。结果这项组合的计算/实验研究将提供详细的原子图。纳米运动膜。提出的分子修饰将导致下一代纳米运动，为临床研究开放这项技术，导致高度选择性的光激活药物。