1
|
Jorgensen C, Troendle EP, Ulmschneider JP, Searson PC, Ulmschneider MB. A least-squares-fitting procedure for an efficient preclinical ranking of passive transport across the blood-brain barrier endothelium. J Comput Aided Mol Des 2023; 37:537-549. [PMID: 37573260 PMCID: PMC10505096 DOI: 10.1007/s10822-023-00525-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/24/2023] [Indexed: 08/14/2023]
Abstract
The treatment of various disorders of the central nervous system (CNS) is often impeded by the limited brain exposure of drugs, which is regulated by the human blood-brain barrier (BBB). The screening of lead compounds for CNS penetration is challenging due to the biochemical complexity of the BBB, while experimental determination of permeability is not feasible for all types of compounds. Here we present a novel method for rapid preclinical screening of libraries of compounds by utilizing advancements in computing hardware, with its foundation in transition-based counting of the flux. This method has been experimentally validated for in vitro permeabilities and provides atomic-level insights into transport mechanisms. Our approach only requires a single high-temperature simulation to rank a compound relative to a library, with a typical simulation time converging within 24 to 72 h. The method offers unbiased thermodynamic and kinetic information to interpret the passive transport of small-molecule drugs across the BBB.
Collapse
Affiliation(s)
- Christian Jorgensen
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark.
| | | | | | - Peter C Searson
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | |
Collapse
|
2
|
Raza A, Chohan TA, Buabeid M, Arafa ESA, Chohan TA, Fatima B, Sultana K, Ullah MS, Murtaza G. Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics. J Biomol Struct Dyn 2023; 41:9177-9192. [PMID: 36305195 DOI: 10.1080/07391102.2022.2136244] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/08/2022] [Indexed: 10/31/2022]
Abstract
Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Ali Raza
- Department of pharmaceutical chemistry, Faculty of Pharmacy, The University of Lahore, Pakistan
- Institute of Molecular Biology and Biochemistry, The University of Lahore, Pakistan
| | - Talha Ali Chohan
- Institute of Molecular Biology and Biochemistry, The University of Lahore, Pakistan
- Institute of Pharmaceutical Science, UVAS, Lahore, Pakistan
| | - Manal Buabeid
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
| | - El-Shaima A Arafa
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
- Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | | | - Batool Fatima
- Department of biochemistry, Bahauddin Zakariya University, Multan, Pakistan
| | - Kishwar Sultana
- Department of pharmaceutical chemistry, Faculty of Pharmacy, The University of Lahore, Pakistan
| | - Malik Saad Ullah
- Department of Pharmacy, Government College University, Faisalabad, Pakistan
| | - Ghulam Murtaza
- Department of Pharmacy, COMSATS University Islamabad, Lahore Campus, Pakistan
| |
Collapse
|
3
|
Pandey P, MacKerell AD. Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules. J Chem Inf Model 2023; 63:5903-5915. [PMID: 37682640 PMCID: PMC10603762 DOI: 10.1021/acs.jcim.3c00514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.
Collapse
Affiliation(s)
- Poonam Pandey
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St., HSF II-633, Baltimore, Maryland 21201, United States
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St., HSF II-633, Baltimore, Maryland 21201, United States
| |
Collapse
|
4
|
Mostofian B, Martin HJ, Razavi A, Patel S, Allen B, Sherman W, Izaguirre JA. Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods. J Chem Inf Model 2023; 63:5408-5432. [PMID: 37602861 PMCID: PMC10498452 DOI: 10.1021/acs.jcim.3c00603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Indexed: 08/22/2023]
Abstract
The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.
Collapse
Affiliation(s)
- Barmak Mostofian
- OpenEye, Cadence Molecular Sciences, Boston, Massachusetts 02114 United States
| | - Holli-Joi Martin
- Laboratory
for Molecular Modeling, Division of Chemical Biology and Medicinal
Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599 United States
| | - Asghar Razavi
- ENKO
Chem, Inc, Mystic, Connecticut 06355 United States
| | - Shivam Patel
- Psivant
Therapeutics, Boston, Massachusetts 02210 United States
| | - Bryce Allen
- Differentiated
Therapeutics, San Diego, California 92056 United States
| | - Woody Sherman
- Psivant
Therapeutics, Boston, Massachusetts 02210 United States
| | - Jesus A Izaguirre
- Differentiated
Therapeutics, San Diego, California 92056 United States
- Atommap
Corporation, New York, New York 10013 United States
| |
Collapse
|
5
|
Niu C, Li X, Dai R, Wang Z. Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review. WATER RESEARCH 2022; 216:118299. [PMID: 35325824 DOI: 10.1016/j.watres.2022.118299] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/11/2022] [Accepted: 03/13/2022] [Indexed: 05/26/2023]
Abstract
Membrane fouling is one of major obstacles in the application of membrane technologies. Accurately predicting or simulating membrane fouling behaviours is of great significance to elucidate the fouling mechanisms and develop effective measures to control fouling. Although mechanistic/mathematical models have been widely used for predicting membrane fouling, they still suffer from low accuracy and poor sensitivity. To overcome the limitations of conventional mathematical models, artificial intelligence (AI)-based techniques have been proposed as powerful approaches to predict membrane filtration performance and fouling behaviour. This work aims to present a state-of-the-art review on the advances in AI algorithms (e.g., artificial neural networks, fuzzy logic, genetic programming, support vector machines and search algorithms) for prediction of membrane fouling. The working principles of different AI techniques and their applications for prediction of membrane fouling in different membrane-based processes are discussed in detail. Furthermore, comparisons of the inputs, outputs, and accuracy of different AI approaches for membrane fouling prediction have been conducted based on the literature database. Future research efforts are further highlighted for AI-based techniques aiming for a more accurate prediction of membrane fouling and the optimization of the operation in membrane-based processes.
Collapse
Affiliation(s)
- Chengxin Niu
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Xuesong Li
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Ruobin Dai
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Zhiwei Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201210, China.
| |
Collapse
|
6
|
A Simulation Model for the Non-Electrogenic Uniport Carrier-Assisted Transport of Ions across Lipid Membranes. MEMBRANES 2022; 12:membranes12030292. [PMID: 35323767 PMCID: PMC8955484 DOI: 10.3390/membranes12030292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 12/10/2022]
Abstract
Impressive work has been completed in recent decades on the transmembrane anion transport capability of small synthetic transporters from many different structural classes. However, very few predicting models have been proposed for the fast screening of compound libraries before spending time and resources on the laboratory bench for their synthesis. In this work, a new approach is presented which aims at describing the transport process by taking all the steps into explicit consideration, and includes all possible experiment-derived parameters. The algorithm is able to simulate the macroscopic experiments performed with lipid vesicles to assess the ion-transport ability of the synthetic transporters following a non-electrogenic uniport mechanism. While keeping calculation time affordable, the final goal is the curve-fitting of real experimental data—so, to obtain both an analysis and a predictive tool. The role and the relative weight of the different parameters is discussed and the agreement with the literature is shown by using the simulations of a virtual benchmark case. The fitting of real experimental curves is also shown for two transporters of different structural type.
Collapse
|
7
|
Jorgensen C, Ulmschneider MB, Searson PC. Atomistic Model of Solute Transport across the Blood-Brain Barrier. ACS OMEGA 2022; 7:1100-1112. [PMID: 35036773 PMCID: PMC8757349 DOI: 10.1021/acsomega.1c05679] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/25/2021] [Indexed: 06/14/2023]
Abstract
The blood-brain barrier remains a major roadblock to the delivery of drugs to the brain. While in vitro and in vivo measurements of permeability are widely used to predict brain penetration, very little is known about the mechanisms of passive transport. Detailed insight into interactions between solutes and cell membranes could provide new insight into drug design and screening. Here, we perform unbiased atomistic MD simulations to visualize translocation of a library of 24 solutes across a lipid bilayer representative of brain microvascular endothelial cells. A temperature bias is used to achieve steady state of all solutes, including those with low permeability. Based on free-energy surface profiles, we show that the solutes can be classified into three groups that describe distinct mechanisms of transport across the bilayer. Simulations down to 310 K for solutes with fast permeability were used to justify the extrapolation of values at 310 K from higher temperatures. Comparison of permeabilities at 310 K to experimental values obtained from in vitro transwell measurements and in situ brain perfusion revealed that permeabilities obtained from simulations vary from close to the experimental values to more than 3 orders of magnitude faster. The magnitude of the difference was dependent on the group defined by free-energy surface profiles. Overall, these results show that MD simulations can provide new insight into the mechanistic details of brain penetration and provide a new approach for drug discovery.
Collapse
Affiliation(s)
- Christian Jorgensen
- Institute
for Nanobiotechnology, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | | | - Peter C. Searson
- Institute
for Nanobiotechnology, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department
of Materials Science and Engineering, Johns
Hopkins University, Baltimore, Maryland 21218, United States
| |
Collapse
|
8
|
Róg T, Girych M, Bunker A. Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design. Pharmaceuticals (Basel) 2021; 14:1062. [PMID: 34681286 PMCID: PMC8537670 DOI: 10.3390/ph14101062] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/14/2021] [Accepted: 10/15/2021] [Indexed: 11/17/2022] Open
Abstract
We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.
Collapse
Affiliation(s)
- Tomasz Róg
- Department of Physics, University of Helsinki, 00014 Helsinki, Finland;
| | - Mykhailo Girych
- Department of Physics, University of Helsinki, 00014 Helsinki, Finland;
| | - Alex Bunker
- Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, 00014 Helsinki, Finland;
| |
Collapse
|
9
|
Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
Collapse
Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| |
Collapse
|
10
|
Lind C, Pandey P, Pastor RW, MacKerell AD. Functional Group Distributions, Partition Coefficients, and Resistance Factors in Lipid Bilayers Using Site Identification by Ligand Competitive Saturation. J Chem Theory Comput 2021; 17:3188-3202. [PMID: 33929848 DOI: 10.1021/acs.jctc.1c00089] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Small molecules such as metabolites and drugs must pass through the membrane of the cell, a barrier primarily comprising phospholipid bilayers and embedded proteins. To better understand the process of passive diffusion, knowledge of the ability of various functional groups to partition across bilayers and the associated energetics would be of utility. In the present study, the site identification by ligand competitive saturation (SILCS) methodology has been applied to sample the distributions of a diverse set of chemical solutes representing the functional groups of small molecules across phospholipid bilayers composed of 0.9:0.1 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine/cholesterol and a mixture of 0.52:0.18:0.3 1,2-dioleoyl-sn-glycero-3-phospho-l-serine/1,2-dioleoyl-sn-glycero-3-phosphocholine/cholesterol used in parallel artificial membrane permeability assay experiments. A combination of oscillating chemical potential grand canonical Monte Carlo and molecular dynamics in the SILCS simulations was applied to achieve solute sampling through the bilayers and surrounding aqueous environment from which the distribution of solutes and the functional groups they represent were obtained. Results show differential distribution of aliphatic versus aromatic groups with the former having increased sampling in the center of the bilayers versus in the region of the glycerol linker for the latter. Variations in the distribution of different polar groups are evident, with large differences between negative acetate and positive methylammonium with accumulation of the polar-neutral and acetate solutes above the bilayer head groups. Conversion of the distributions to absolute free energies allows for a detailed understanding of energetics of functional groups in different regions of the bilayers and for calculation of absolute free-energy profiles of multifunctional drug-like molecules across the bilayers from which partition coefficients and resistance factors suitable for insertion into the homogenous solubility-diffusion equation for calculation of permeability were obtained. Comparisons of the calculated bilayer/solution partition coefficients with 1-octanol/water experimental data for both drug-like molecules and the solutes show overall good agreement, validating the calculated distributions and associated absolute free-energy profiles.
Collapse
Affiliation(s)
- Christoffer Lind
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Baltimore, Maryland 21201, United States
| | - Poonam Pandey
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Baltimore, Maryland 21201, United States
| | - Richard W Pastor
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Baltimore, Maryland 21201, United States
| |
Collapse
|
11
|
Sharifian Gh M. Recent Experimental Developments in Studying Passive Membrane Transport of Drug Molecules. Mol Pharm 2021; 18:2122-2141. [PMID: 33914545 DOI: 10.1021/acs.molpharmaceut.1c00009] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The ability to measure the passive membrane permeation of drug-like molecules is of fundamental biological and pharmaceutical importance. Of significance, passive diffusion across the cellular membranes plays an effective role in the delivery of many pharmaceutical agents to intracellular targets. Hence, approaches for quantitative measurement of membrane permeability have been the topics of research for decades, resulting in sophisticated biomimetic systems coupled with advanced techniques. In this review, recent developments in experimental approaches along with theoretical models for quantitative and real-time analysis of membrane transport of drug-like molecules through mimetic and living cell membranes are discussed. The focus is on time-resolved fluorescence-based, surface plasmon resonance, and second-harmonic light scattering approaches. The current understanding of how properties of the membrane and permeant affect the permeation process is discussed.
Collapse
Affiliation(s)
- Mohammad Sharifian Gh
- Department of Cell Biology, University of Virginia, Charlottesville, Virginia 22908, United States
| |
Collapse
|
12
|
Kamrava S, Tahmasebi P, Sahimi M. Physics- and image-based prediction of fluid flow and transport in complex porous membranes and materials by deep learning. J Memb Sci 2021. [DOI: 10.1016/j.memsci.2021.119050] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
13
|
Hotarat W, Nutho B, Wolschann P, Rungrotmongkol T, Hannongbua S. Delivery of Alpha-Mangostin Using Cyclodextrins through a Biological Membrane: Molecular Dynamics Simulation. Molecules 2020; 25:molecules25112532. [PMID: 32485931 PMCID: PMC7321106 DOI: 10.3390/molecules25112532] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 05/27/2020] [Accepted: 05/28/2020] [Indexed: 12/14/2022] Open
Abstract
α-Mangostin (MGS) exhibits various pharmacological activities, including antioxidant, anticancer, antibacterial, and anti-inflammatory properties. However, its low water solubility is the major obstacle for its use in pharmaceutical applications. To increase the water solubility of MGS, complex formation with beta-cyclodextrins (βCDs), particularly with the native βCD and/or its derivative 2,6-dimethyl-β-CD (DMβCD) is a promising technique. Although there have been several reports on the adsorption of βCDs on the lipid bilayer, the release of the MGS/βCDs inclusion complex through the biological membrane remains unclear. In this present study, the release the MGS from the two different βCDs (βCD and DMβCD) across the lipid bilayer was investigated. Firstly, the adsorption of the free MGS, free βCDs, and inclusion complex formation was studied by conventional molecular dynamics simulation. The MGS in complex with those two βCDs was able to spontaneously release free MGS into the inner membrane. However, both MGS and DMβCD molecules potentially permeated into the deeper region of the interior membrane, whereas βCD only adsorbed at the outer membrane surface. The interaction between secondary rim of βCD and the 1-palmitoeyl-2-oleoyl-glycero-3-phosphocholine (POPC) phosphate groups showed the highest number of hydrogen bonds (up to 14) corresponding to the favorable location of βCD on the POPC membrane. Additionally, the findings suggested that electrostatic energy was the main driving force for βCD adsorption on the POPC membrane, while van der Waals interactions played a predominant role in DMβCD adsorption. The release profile of MGS from the βCDs pocket across the lipid bilayer exhibited two energy minima along the reaction coordinate associated with the permeation of the MGS molecule into the deeper region of the POPC membrane.
Collapse
Affiliation(s)
- Wiparat Hotarat
- Center of Excellence in Computational Chemistry (CECC), Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand; (W.H.); (B.N.)
| | - Bodee Nutho
- Center of Excellence in Computational Chemistry (CECC), Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand; (W.H.); (B.N.)
| | - Peter Wolschann
- Department of Pharmaceutical Chemistry, University of Vienna, 1090 Vienna, Austria;
- Institute of Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria
| | - Thanyada Rungrotmongkol
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand
- Structural and Computational Biology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Molecular Sensory Science Center, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Correspondence: (T.R.); (S.H.); Tel.: +66(0)2218-5418 (T.R.); +66(0)2218-7603 (S.H.)
| | - Supot Hannongbua
- Center of Excellence in Computational Chemistry (CECC), Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand; (W.H.); (B.N.)
- Correspondence: (T.R.); (S.H.); Tel.: +66(0)2218-5418 (T.R.); +66(0)2218-7603 (S.H.)
| |
Collapse
|
14
|
Affiliation(s)
- Günter Klambauer
- Johannes Kepler University , LIT AI Lab & Institute for Machine Learning , 4040 Linz , Austria
| | - Sepp Hochreiter
- Johannes Kepler University , LIT AI Lab & Institute for Machine Learning , 4040 Linz , Austria
| | - Matthias Rarey
- Universität Hamburg , ZBH-Center for Bioinformatics , 20146 Hamburg , Germany
| |
Collapse
|
15
|
Tang PK, Chakraborty K, Hu W, Kang M, Loverde SM. Interaction of Camptothecin with Model Cellular Membranes. J Chem Theory Comput 2020; 16:3373-3384. [PMID: 32126167 DOI: 10.1021/acs.jctc.9b00541] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Accurate and efficient prediction of drug partitioning in model membranes is of significant interest to the pharmaceutical industry. Herein, we utilize advanced sampling methods, specifically, the adaptive biasing force methodology to calculate the potential of mean force for a model hydrophobic anticancer drug, camptothecin (CPT), across three model interfaces. We consider an octanol bilayer, a thick octanol/water interface, and a model 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC)/water interface. We characterize the enthalpic and entropic contributions of the drug to the potential of mean force. We show that the rotational entropy of the drug is inversely related to the probability of hydrogen bond formation of the drug with the POPC membrane. In addition, in long-time microsecond simulations of a high concentration of CPT above the POPC membrane, we show that strong drug-drug aromatic interactions shift the spatial orientation of the drug with the membrane. Stacks of hydrophobic drugs form, allowing penetration of the drug just under the POPC head groups. These results imply that inhomogeneous membrane models need to take into account the effect of drug aggregation on the membrane environment.
Collapse
Affiliation(s)
- Phu K Tang
- Department of Chemistry, College of Staten Island, City University of New York, 2800 Victory Boulevard, 6S-238, Staten Island, New York 10314, United States.,Ph.D. Program in Chemistry, Biochemistry, and Physics, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Kaushik Chakraborty
- Department of Chemistry, College of Staten Island, City University of New York, 2800 Victory Boulevard, 6S-238, Staten Island, New York 10314, United States
| | - William Hu
- Hunter College High School, New York, New York, 10128, United States
| | - Myungshim Kang
- Department of Chemistry, College of Staten Island, City University of New York, 2800 Victory Boulevard, 6S-238, Staten Island, New York 10314, United States
| | - Sharon M Loverde
- Department of Chemistry, College of Staten Island, City University of New York, 2800 Victory Boulevard, 6S-238, Staten Island, New York 10314, United States.,Department of Physics, Graduate Center, City University of New York, 365 Fifth Avenue, New York, New York 10016, United States.,Ph.D. Program in Chemistry, Biochemistry, and Physics, The Graduate Center of the City University of New York, New York, New York 10016, United States
| |
Collapse
|
16
|
Dp44mT, an iron chelator, suppresses growth and induces apoptosis via RORA-mediated NDRG2-IL6/JAK2/STAT3 signaling in glioma. Cell Oncol (Dordr) 2020; 43:461-475. [PMID: 32207044 DOI: 10.1007/s13402-020-00502-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 02/08/2020] [Accepted: 03/10/2020] [Indexed: 12/13/2022] Open
Abstract
PURPOSE The iron-chelating agent di-2-pyridylketone 4,4-dimethyl-3-thiosemicarbazone (Dp44mT) has been found to inhibit cell growth and to induce apoptosis in several human cancers. However, its effects and mechanism of action in glioma are unknown. METHODS Human glioma cell line LN229 and patient-derived glioma stem cells GSC-42 were applied for both in vitro and in vivo xenograft nude mouse experiments. The anti-tumor effects of Dp44mT were assessed using MTS, EdU, TUNEL, Western blotting, qRT-PCR, luciferase reporter, chromatin immunoprecipitation and immunohistochemical assays. RESULTS We found that Dp44mT can upregulate the expression of the anti-oncogene N-myc downstream-regulated gene (NDRG)2 by directly binding to and activating the RAR-related orphan receptor (ROR)A. In addition, we found that NDRG2 overexpression suppressed inflammation via activation of interleukin (IL)-6/Janus kinase (JAK)2/signal transducer and activator of transcription (STAT)3 signaling. CONCLUSIONS Our data indicate that Dp44mT may serve as an effective drug for the treatment of glioma by targeting RORA and enhancing NDRG2-mediated IL-6/JAK2/STAT3 signaling.
Collapse
|
17
|
Lomize AL, Hage JM, Schnitzer K, Golobokov K, LaFaive MB, Forsyth AC, Pogozheva ID. PerMM: A Web Tool and Database for Analysis of Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules. J Chem Inf Model 2019; 59:3094-3099. [PMID: 31259547 DOI: 10.1021/acs.jcim.9b00225] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The PerMM web server and database were developed for quantitative analysis and visualization of passive translocation of bioactive molecules across lipid membranes. The server is the first physics-based web tool that calculates membrane binding energies and permeability coefficients of diverse molecules through artificial and natural membranes (phospholipid bilayers, PAMPA-DS, blood-brain barrier, and Caco-2/MDCK cell membranes). It also visualizes the transmembrane translocation pathway as a sequence of translational and rotational positions of a permeant as it moves across the lipid bilayer, along with the corresponding changes in solvation energy. The server can be applied for prediction of permeability coefficients of compounds with diverse chemical scaffolds to facilitate selection and optimization of potential drug leads. The complementary PerMM database allows comparison of computationally and experimentally determined permeability coefficients for more than 500 compounds in different membrane systems. The website and database are freely accessible at https://permm.phar.umich.edu/ .
Collapse
Affiliation(s)
- Andrei L Lomize
- Department of Medicinal Chemistry, College of Pharmacy , University of Michigan , 428 Church Street , Ann Arbor , Michigan 48109-1065 , United States
| | - Jacob M Hage
- Department of Electrical Engineering and Computer Science, College of Engineering , University of Michigan , 1221 Beal Ave , Ann Arbor , Michigan 48109-2102 , United States
| | - Kevin Schnitzer
- Department of Electrical Engineering and Computer Science, College of Engineering , University of Michigan , 1221 Beal Ave , Ann Arbor , Michigan 48109-2102 , United States
| | - Konstantin Golobokov
- Department of Electrical Engineering and Computer Science, College of Engineering , University of Michigan , 1221 Beal Ave , Ann Arbor , Michigan 48109-2102 , United States
| | - Mitchell B LaFaive
- Department of Electrical Engineering and Computer Science, College of Engineering , University of Michigan , 1221 Beal Ave , Ann Arbor , Michigan 48109-2102 , United States
| | - Alexander C Forsyth
- Department of Computer Science, College of Literature, Science, and the Arts , University of Michigan , 2260 Hayward Street , Ann Arbor , Michigan 48109-2121 , United States
| | - Irina D Pogozheva
- Department of Medicinal Chemistry, College of Pharmacy , University of Michigan , 428 Church Street , Ann Arbor , Michigan 48109-1065 , United States
| |
Collapse
|
18
|
Lomize AL, Pogozheva ID. Physics-Based Method for Modeling Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules. J Chem Inf Model 2019; 59:3198-3213. [PMID: 31259555 DOI: 10.1021/acs.jcim.9b00224] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Assessment of permeability is a critical step in the drug development process for selection of drug candidates with favorable ADME properties. We have developed a novel physics-based method for fast computational modeling of passive permeation of diverse classes of molecules across lipid membranes. The method is based on heterogeneous solubility-diffusion theory and operates with all-atom 3D structures of solutes and the anisotropic solvent model of the lipid bilayer characterized by transbilayer profiles of dielectric and hydrogen bonding capacity parameters. The optimal translocation pathway of a solute is determined by moving an ensemble of representative conformations of the molecule through the dioleoyl-phosphatidylcholine (DOPC) bilayer and optimizing their rotational orientations in every point of the transmembrane trajectory. The method calculates (1) the membrane-bound state of the solute molecule; (2) free energy profile of the solute along the permeation pathway; and (3) the permeability coefficient obtained by integration over the transbilayer energy profile and assuming a constant size-dependent diffusivity along the membrane normal. The accuracy of the predictions was evaluated against experimental permeability coefficients measured in pure lipid membranes (for 78 compounds, R2 was 0.88 and rmse was 1.15 log units), PAMPA-DS (for 280 compounds, R2 was 0.75 and rmse was 1.59 log units), BBB (for 182 compounds, R2 was 0.69 and rmse was 0.87 log units), and Caco-2/MDCK assays (for 165 compounds, R2 was 0.52 and rmse was 0.89 log units).
Collapse
Affiliation(s)
- Andrei L Lomize
- Department of Medicinal Chemistry, College of Pharmacy , University of Michigan , 428 Church Street , Ann Arbor , Michigan 48109-1065 , United States
| | - Irina D Pogozheva
- Department of Medicinal Chemistry, College of Pharmacy , University of Michigan , 428 Church Street , Ann Arbor , Michigan 48109-1065 , United States
| |
Collapse
|