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.

项目概要/摘要迫切需要开发新的药物治疗分子范例来解决贫困问题药物选择性，造成副作用、耐药性和环境毒性，目前85%以上的药物。由于选择性差而在临床研究中被丢弃是现代人关注的主要问题。光药理学通过使用受控光激活药物来提高药物选择性。最近，开发了一种光驱动的分子纳米马达（García-）。 López et al., Nature, 548, 7669, 567, 2017)，能够破坏生物膜，诱导细胞这种机制在通过脂质纳米粒子递送药物方面具有潜在的应用。除了化疗、放疗、手术、机械治疗外，还包括癌症治疗和传染病治疗。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。由于仍处于发展阶段，因此需要详细了解该过程的分子机制我们将使用计算机建模来研究这项技术的临床应用。基于最近开发的纳米马达破坏膜的分子机制。洞察力，我们将通过引入官能团来设计和优化新的纳米电机，以改善分子性能最终目标是开发可应用于临床研究的下一代纳米电机。降低光毒性，电机必须以高量子产率运行，从而实现高百分比- 吸收的光子的年龄转化为机械功以取代膜脂质此外，组织应用。要求照射波长发生在 600–1000 nm 区域，其穿透深度比最初使用紫外线，它也具有更高的光毒性，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模拟。膜中纳米电机的光诱导动力学，产生有关膜的原子信息为此，我们将使用机器学习驱动的分子动力学。根据所获得的见解，将使用量子力学模拟来训练算法。通过官能团的修饰可以增强特性：a) 与膜的结合亲和力；b) 近红外或可见光区域的光吸收；c) 吸收截面和量子产率。我们将在基于详尽分子生成的计算机高通量筛选中采用候选分子以及量子力学特性的机器学习将被合成具有改进特性的候选物。由 García-López 实验室确定尺寸并进行实验研究以衡量预测结果的有效性。这项综合计算/实验研究将给出动力学的详细原子图所提出的分子修饰将导致下一代纳米电机，将该技术开放用于临床研究，从而产生高选择性的光激活药物。