1
|
Handa K, Fujita D, Hirano M, Yoshimura S, Kageyama M, Iijima T. A Practical In Silico Method for Predicting Compound Brain Concentration-Time Profiles: Combination of PK Modeling and Machine Learning. Mol Pharm 2024; 21:5182-5191. [PMID: 39324316 DOI: 10.1021/acs.molpharmaceut.4c00584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2024]
Abstract
Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration-time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration-time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an in silico prediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration-time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration-time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration-time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration-time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration-time profiles.
Collapse
Affiliation(s)
- Koichi Handa
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
| | - Daichi Fujita
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
| | - Mariko Hirano
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
| | - Saki Yoshimura
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
| | - Michiharu Kageyama
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
| | - Takeshi Iijima
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
| |
Collapse
|
2
|
Das B, Mathew AT, Baidya ATK, Devi B, Salmon RR, Kumar R. Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study. Mol Divers 2024; 28:2013-2031. [PMID: 37022608 DOI: 10.1007/s11030-023-10645-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 03/29/2023] [Indexed: 04/07/2023]
Abstract
Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major pathological hallmarks of AD and has gained the interest of researchers as a potential drug target to treat AD. Lately, Artificial Intelligence (AI) has revolutionized the drug discovery process by speeding it up and reducing the overall cost. As a part of our continuous effort to identify potential tau aggregation inhibitors, and leveraging the power of AI, in this study, we used a fully automated AI-assisted ligand-based virtual screening tool, PyRMD to screen a library of 12 million compounds from the ZINC database to identify potential tau aggregation inhibitors. The preliminary hits from virtual screening were filtered for similar compounds and pan-assay interference compounds (the compounds containing reactive functional groups which can interfere with the assays) using RDKit. Further, the selected compounds were prioritized based on their molecular docking score with the binding pocket of tau where the binding pockets were identified using replica exchange molecular dynamics simulation. Thirty-three compounds showing good docking scores for all the tau clusters were selected and were further subjected to in silico pharmacokinetic prediction. Finally, top 10 compounds were selected for molecular dynamics simulation and MMPBSA binding free energy calculations resulting in the identification of UNK_175, UNK_1027, UNK_1172, UNK_1173, UNK_1237, UNK_1518, and UNK_2181 as potential tau aggregation inhibitors.
Collapse
Affiliation(s)
- Bhanuranjan Das
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India
| | - Alen T Mathew
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India
| | - Anurag T K Baidya
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India
| | - Bharti Devi
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India
| | - Rahul Rampa Salmon
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India
| | - Rajnish Kumar
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India.
| |
Collapse
|
3
|
Devi B, Vasishta SS, Das B, Baidya ATK, Rampa RS, Mahapatra MK, Kumar R. Integrated use of ligand and structure-based virtual screening, molecular dynamics, free energy calculation and ADME prediction for the identification of potential PTP1B inhibitors. Mol Divers 2024; 28:649-669. [PMID: 36745307 DOI: 10.1007/s11030-023-10608-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 01/20/2023] [Indexed: 02/07/2023]
Abstract
Protein tyrosine phosphatases (PTPs) are the group of enzymes that control both cellular activity and the dephosphorylation of tyrosine (Tyr)-phosphorylated proteins. Dysregulation of PTP1B has contributed to numerous diseases including Diabetes Mellitus, Alzheimer's disease, and obesity rendering PTP1B as a legitimate target for therapeutic applications. It is highly challenging to target this enzyme because of its highly conserved and positively charged active-site pocket motivating researchers to find novel lead compounds against it. The present work makes use of an integrated approach combining ligand-based and structure-based virtual screening to find hit compounds targeting PTP1B. Initially, pharmacophore modeling was performed to find common features like two hydrogen bond acceptors, an aromatic ring and one hydrogen bond donor from the potent PTP1B inhibitors. The dataset of compounds matching with the common pharmacophoric features was filtered to remove Pan-Assay Interference substructure and to match the Lipinski criteria. Then, compounds were further prioritized using molecular docking and top fifty compounds with good binding affinity were selected for absorption, distribution, metabolism, and excretion (ADME) predictions. The top five compounds with high solubility, absorption and permeability holding score of - 10 to - 9.3 kcal/mol along with Ertiprotafib were submitted to all-atom molecular dynamic (MD) studies. The MD studies and binding free energy calculations showed that compound M4, M5 and M8 were having better binding affinity for PTP1B enzyme with ∆Gtotal score of - 24.25, - 31.47 and - 33.81 kcal/mol respectively than other compounds indicating that compound M8 could be a suitable lead compound as PTP1B inhibitor.
Collapse
Affiliation(s)
- Bharti Devi
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP, 221005, India
| | - Sumukh Satyanarayana Vasishta
- Department of Chemical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP, 221005, India
| | - Bhanuranjan Das
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP, 221005, India
| | - Anurag T K Baidya
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP, 221005, India
| | - Rahul Salmon Rampa
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP, 221005, India
| | | | - Rajnish Kumar
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP, 221005, India.
| |
Collapse
|
4
|
Ma Y, Jiang M, Javeria H, Tian D, Du Z. Accurate prediction of K p,uu,brain based on experimental measurement of K p,brain and computed physicochemical properties of candidate compounds in CNS drug discovery. Heliyon 2024; 10:e24304. [PMID: 38298681 PMCID: PMC10828645 DOI: 10.1016/j.heliyon.2024.e24304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 02/02/2024] Open
Abstract
A mathematical equation model was developed by building the relationship between the fu,b/fu,p ratio and the computed physicochemical properties of candidate compounds, thereby predicting Kp,uu,brain based on a single experimentally measured Kp,brain value. A total of 256 compounds and 36 marketed published drugs including acidic, basic, neutral, zwitterionic, CNS-penetrant, and non-CNS penetrant compounds with diverse structures and physicochemical properties were involved in this study. A strong correlation was demonstrated between the fu,b/fu,p ratio and physicochemical parameters (CLogP and ionized fraction). The model showed good performance in both internal and external validations. The percentages of compounds with Kp,uu,brain predictions within 2-fold variability were 80.0 %-83.3 %, and more than 90 % were within a 3-fold variability. Meanwhile, "black box" QSAR models constructed by machine learning approaches for predicting fu,b/fu,p ratio based on the chemical descriptors are also presented, and the ANN model displayed the highest accuracy with an RMSE value of 0.27 and 86.7 % of the test set drugs fell within a 2-fold window of linear regression. These models demonstrated strong predictive power and could be helpful tools for evaluating the Kp,uu,brain by a single measurement parameter of Kp,brain during lead optimization for CNS penetration evaluation and ranking CNS drug candidate molecules in the early stages of CNS drug discovery.
Collapse
Affiliation(s)
- Yongfen Ma
- College of Chemistry, Beijing Key Laboratory of Environmentally Harmful Chemical Analysis, Beijing University of Chemical Technology, Beijing, 100029, China
- DMPK Department, Sironax (Beijing) Co., Ltd, Beijing, 102206, China
| | - Mengrong Jiang
- DMPK Department, Sironax (Beijing) Co., Ltd, Beijing, 102206, China
| | - Huma Javeria
- College of Chemistry, Beijing Key Laboratory of Environmentally Harmful Chemical Analysis, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Dingwei Tian
- College of Chemistry, Beijing Key Laboratory of Environmentally Harmful Chemical Analysis, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Zhenxia Du
- College of Chemistry, Beijing Key Laboratory of Environmentally Harmful Chemical Analysis, Beijing University of Chemical Technology, Beijing, 100029, China
| |
Collapse
|
5
|
Kotze S, Ebert A, Goss KU. Effects of Aqueous Boundary Layers and Paracellular Transport on the Efflux Ratio as a Measure of Active Transport Across Cell Layers. Pharmaceutics 2024; 16:132. [PMID: 38276501 PMCID: PMC11154460 DOI: 10.3390/pharmaceutics16010132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/15/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
The efflux ratio (ER), determined by Caco-2/MDCK assays, is the standard in vitro metric to establish qualitatively whether a compound is a substrate of an efflux transporter. However, others have also enabled the utilisation of this metric quantitatively by deriving a relationship that expresses the ER as a function of the intrinsic membrane permeability of the membrane (P0) as well as the permeability of carrier-mediated efflux (Ppgp). As of yet, Ppgp cannot be measured directly from transport experiments or otherwise, but the ER relationship provides easy access to this value if P0 is known. However, previous derivations of this relationship failed to consider the influence of additional transport resistances such as the aqueous boundary layers (ABLs) and the filter on which the monolayer is grown. Since single fluxes in either direction can be heavily affected by these experimental artefacts, it is crucial to consider the potential impact on the ER. We present a model that includes these factors and show both mathematically and experimentally that this simple ER relationship also holds for the more realistic scenario that does not neglect the ABLs/filter. Furthermore, we also show mathematically how paracellular transport affects the ER, and we experimentally confirm that paracellular dominance reduces the ER to unity and can mask potential efflux.
Collapse
Affiliation(s)
- Soné Kotze
- Department of Analytical Environmental Chemistry, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, 04318 Leipzig, Germany; (S.K.); (A.E.)
| | - Andrea Ebert
- Department of Analytical Environmental Chemistry, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, 04318 Leipzig, Germany; (S.K.); (A.E.)
| | - Kai-Uwe Goss
- Department of Analytical Environmental Chemistry, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, 04318 Leipzig, Germany; (S.K.); (A.E.)
- Institute of Chemistry, University of Halle-Wittenberg, Kurt-Mothes-Straße 2, 06120 Halle, Germany
| |
Collapse
|
6
|
Komura H, Watanabe R, Mizuguchi K. The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery. Pharmaceutics 2023; 15:2619. [PMID: 38004597 PMCID: PMC10675155 DOI: 10.3390/pharmaceutics15112619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.
Collapse
Affiliation(s)
- Hiroshi Komura
- University Research Administration Center, Osaka Metropolitan University, 1-2-7 Asahimachi, Abeno-ku, Osaka 545-0051, Osaka, Japan
| | - Reiko Watanabe
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
| | - Kenji Mizuguchi
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
| |
Collapse
|
7
|
Veiga-Matos J, Morales AI, Prieto M, Remião F, Silva R. Study Models of Drug-Drug Interactions Involving P-Glycoprotein: The Potential Benefit of P-Glycoprotein Modulation at the Kidney and Intestinal Levels. Molecules 2023; 28:7532. [PMID: 38005253 PMCID: PMC10673607 DOI: 10.3390/molecules28227532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
P-glycoprotein (P-gp) is a crucial membrane transporter situated on the cell's apical surface, being responsible for eliminating xenobiotics and endobiotics. P-gp modulators are compounds that can directly or indirectly affect this protein, leading to changes in its expression and function. These modulators can act as inhibitors, inducers, or activators, potentially causing drug-drug interactions (DDIs). This comprehensive review explores diverse models and techniques used to assess drug-induced P-gp modulation. We cover several approaches, including in silico, in vitro, ex vivo, and in vivo methods, with their respective strengths and limitations. Additionally, we explore the therapeutic implications of DDIs involving P-gp, with a special focus on the renal and intestinal elimination of P-gp substrates. This involves enhancing the removal of toxic substances from proximal tubular epithelial cells into the urine or increasing the transport of compounds from enterocytes into the intestinal lumen, thereby facilitating their excretion in the feces. A better understanding of these interactions, and of the distinct techniques applied for their study, will be of utmost importance for optimizing drug therapy, consequently minimizing drug-induced adverse and toxic effects.
Collapse
Affiliation(s)
- Jéssica Veiga-Matos
- UCIBIO-Applied Molecular Biosciences Unit, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal;
- Associate Laboratory i4HB—Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- Toxicology Unit (Universidad de Salamanca), Group of Translational Research on Renal and Cardiovascular Diseases (TRECARD), Institute of Biomedical Research of Salamanca (IBSAL), 37007 Salamanca, Spain; (A.I.M.); (M.P.)
| | - Ana I. Morales
- Toxicology Unit (Universidad de Salamanca), Group of Translational Research on Renal and Cardiovascular Diseases (TRECARD), Institute of Biomedical Research of Salamanca (IBSAL), 37007 Salamanca, Spain; (A.I.M.); (M.P.)
| | - Marta Prieto
- Toxicology Unit (Universidad de Salamanca), Group of Translational Research on Renal and Cardiovascular Diseases (TRECARD), Institute of Biomedical Research of Salamanca (IBSAL), 37007 Salamanca, Spain; (A.I.M.); (M.P.)
| | - Fernando Remião
- UCIBIO-Applied Molecular Biosciences Unit, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal;
- Associate Laboratory i4HB—Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| | - Renata Silva
- UCIBIO-Applied Molecular Biosciences Unit, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal;
- Associate Laboratory i4HB—Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| |
Collapse
|
8
|
Mamada H, Takahashi M, Ogino M, Nomura Y, Uesawa Y. Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening. ACS OMEGA 2023; 8:37186-37195. [PMID: 37841172 PMCID: PMC10568689 DOI: 10.1021/acsomega.3c04073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 08/30/2023] [Indexed: 10/17/2023]
Abstract
Various toxicity and pharmacokinetic evaluations as screening experiments are needed at the drug discovery stage. Currently, to reduce the use of animal experiments and developmental expenses, the development of high-performance predictive models based on quantitative structure-activity relationship analysis is desired. From these evaluation targets, we selected 50% lethal dose (LD50), blood-brain barrier penetration (BBBP), and the clearance (CL) pathway for this investigation and constructed predictive models for each target using 636-11,886 compounds. First, we constructed predictive models using the DeepSnap-deep learning (DL) method and images of compounds as features. The calculated area under the curve (AUC) and balanced accuracy (BAC) were, respectively, 0.887 and 0.818 for LD50, 0.893 and 0.824 for BBBP, and 0.883 and 0.763 for the CL pathway. Next, molecular descriptors (MDs) of compounds were calculated using Molecular Operating Environment, alvaDesc, and ADMET Predictor to construct predictive models using the MD-based method. Using these MDs, we constructed predictive models using DataRobot. The calculated AUC and BAC were, respectively, 0.931 and 0.805 for LD50, 0.919 and 0.849 for BBBP, and 0.900 and 0.807 for the CL pathway. In this investigation, we constructed predictive models combining the DeepSnap-DL and MD-based methods. In ensemble models using the mean predictive probability of the DeepSnap-DL and MD-based methods, the calculated AUC and BAC were, respectively, 0.942 and 0.842 for LD50, 0.936 and 0.853 for BBBP, and 0.908 and 0.832 for the CL pathway, with improved predictive performance observed for all variables compared with either single method alone. Moreover, in consensus models that adopted only compounds for which the results of the two methods agreed, the calculated BAC for LD50, BBBP, and the CL pathway were 0.916, 0.918, and 0.847, respectively, indicating higher predictive performance than the ensemble models for all three variables. The predictive models combining the DeepSnap-DL and MD-based methods displayed high predictive performance for LD50, BBBP, and the CL pathway. Therefore, the application of this approach to prediction targets in various drug discovery screenings is expected to accelerate drug discovery.
Collapse
Affiliation(s)
- Hideaki Mamada
- Drug
Metabolism and Pharmacokinetics Research Laboratories, Central Pharmaceutical
Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Mari Takahashi
- Drug
Metabolism and Pharmacokinetics Research Laboratories, Central Pharmaceutical
Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Mizuki Ogino
- Drug
Metabolism and Pharmacokinetics Research Laboratories, Central Pharmaceutical
Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Yukihiro Nomura
- Drug
Metabolism and Pharmacokinetics Research Laboratories, Central Pharmaceutical
Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Yoshihiro Uesawa
- Department
of Medical Molecular Informatics, Meiji
Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-858, Japan
| |
Collapse
|
9
|
Liu S, Kosugi Y. Human Brain Penetration Prediction Using Scaling Approach from Animal Machine Learning Models. AAPS J 2023; 25:86. [PMID: 37667061 DOI: 10.1208/s12248-023-00850-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 08/14/2023] [Indexed: 09/06/2023] Open
Abstract
Machine learning (ML) approaches have been applied to predicting drug pharmacokinetic properties. Previously, we predicted rat unbound brain-to-plasma ratio (Kpuu,brain) by ML models. In this study, we aimed to predict human Kpuu,brain through animal ML models. First, we re-evaluated ML models for rat Kpuu,brain prediction by using trendy open-source packages. We then developed ML models for monkey Kpuu,brain prediction. Leave-one-out cross validation was utilized to rationally build models using a relatively small dataset. After establishing the monkey and rat ML models, human Kpuu,brain prediction was achieved by implementing the animal models considering appropriate scaling methods. Mechanistic NeuroPK models for the identical monkey and human dataset were treated as the criteria for comparison. Results showed that rat Kpuu,brain predictivity was successfully replicated. The optimal ML model for monkey Kpuu,brain prediction was superior to the NeuroPK model, where accuracy within 2-fold error was 78% (R2 = 0.76). For human Kpuu,brain prediction, rat model using relative expression factor (REF), scaled transporter efflux ratios (ERs), and monkey model using in vitro ERs can provide comparable predictivity to the NeuroPK model, where accuracy within 2-fold error was 71% and 64% (R2 = 0.30 and 0.52), respectively. We demonstrated that ML models can deliver promising Kpuu,brain prediction with several advantages: (1) predict reasonable animal Kpuu,brain; (2) prospectively predict human Kpuu,brain from animal models; and (3) can skip expensive monkey studies for human prediction by using the rat model. As a result, ML models can be a powerful tool for drug Kpuu,brain prediction in the discovery stage.
Collapse
Affiliation(s)
- Siyu Liu
- Drug Metabolism & Pharmacokinetics Research Laboratories, Preclinical & Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Shonan Health Innovation Park, 26-1, Muraoka-Higashi 2-Chome, Fujisawa, Kanagawa, 251-8555, Japan.
| | - Yohei Kosugi
- Drug Metabolism & Pharmacokinetics Research Laboratories, Preclinical & Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Shonan Health Innovation Park, 26-1, Muraoka-Higashi 2-Chome, Fujisawa, Kanagawa, 251-8555, Japan
| |
Collapse
|
10
|
Kawashima H, Watanabe R, Esaki T, Kuroda M, Nagao C, Natsume-Kitatani Y, Ohashi R, Komura H, Mizuguchi K. DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform. J Med Chem 2023. [PMID: 37449459 PMCID: PMC10388294 DOI: 10.1021/acs.jmedchem.3c00481] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and in-house data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/.
Collapse
Affiliation(s)
- Hitoshi Kawashima
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
| | - Reiko Watanabe
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Tsuyoshi Esaki
- Data Science and AI Innovation Research Promotion Center, Shiga University, Hikone, Shiga 522-8522, Japan
| | - Masataka Kuroda
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
- Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, Yokohama, Kanagawa 227-0033, Japan
| | - Chioko Nagao
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Yayoi Natsume-Kitatani
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
- Institute of Advanced Medical Sciences, Tokushima University, Tokushima, Tokushima 770-8503, Japan
| | - Rikiya Ohashi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
| | - Hiroshi Komura
- University Research Administration Center, Osaka Metropolitan University, Osaka, Osaka 545-0051, Japan
| | - Kenji Mizuguchi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| |
Collapse
|
11
|
Lawrenz M, Svensson M, Kato M, Dingley KH, Chief Elk J, Nie Z, Zou Y, Kaplan Z, Lagiakos HR, Igawa H, Therrien E. A Computational Physics-based Approach to Predict Unbound Brain-to-Plasma Partition Coefficient, K p,uu. J Chem Inf Model 2023. [PMID: 37267072 DOI: 10.1021/acs.jcim.3c00150] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The blood-brain barrier (BBB) plays a critical role in preventing harmful endogenous and exogenous substances from penetrating the brain. Optimal brain penetration of small-molecule central nervous system (CNS) drugs is characterized by a high unbound brain/plasma ratio (Kp,uu). While various medicinal chemistry strategies and in silico models have been reported to improve BBB penetration, they have limited application in predicting Kp,uu directly. We describe a physics-based computational approach, a quantum mechanics (QM)-based energy of solvation (E-sol), to predict Kp,uu. Prospective application of this method in internal CNS drug discovery programs highlights the utility and accuracy of this new method, which showed a categorical accuracy of 79% and an R2 of 0.61 from a linear regression model.
Collapse
Affiliation(s)
- Morgan Lawrenz
- Schrödinger Inc., San Diego, California 92122, United States
| | - Mats Svensson
- Schrödinger Inc., New York, New York 10036, United States
| | - Mitsunori Kato
- Schrödinger Inc., New York, New York 10036, United States
| | | | | | - Zhe Nie
- Schrödinger Inc., San Diego, California 92122, United States
| | - Yefen Zou
- Schrödinger Inc., San Diego, California 92122, United States
| | - Zachary Kaplan
- Schrödinger Inc., New York, New York 10036, United States
| | | | - Hideyuki Igawa
- Schrödinger Inc., New York, New York 10036, United States
| | - Eric Therrien
- Schrödinger Inc., New York, New York 10036, United States
| |
Collapse
|
12
|
Hydrogen Sulfide Attenuates Lipopolysaccharide-Induced Inflammation via the P-glycoprotein and NF-κB Pathway in Astrocytes. Neurochem Res 2022; 48:1424-1437. [PMID: 36482035 PMCID: PMC10066098 DOI: 10.1007/s11064-022-03840-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/13/2022] [Accepted: 11/29/2022] [Indexed: 12/13/2022]
Abstract
AbstractAstrocyte activation is key in neurodegenerative diseases. Hydrogen sulfide (H2S) exhibits neuroprotective effects on astrocytes, although the underlying molecular mechanism remains unclear. Here, we explored the effects of H2S on lipopolysaccharide (LPS)-induced astrocyte activation and astrocyte-mediated neuroinflammation. After inducing primary astrocytes via LPS exposure, H2S levels were altered. The generation and secretion of inflammatory mediators by astrocytes and their interrelation with P-glycoprotein (P-gp), an important transporter belonging to the ABC transporter family, were assessed. Activated astrocytes showed upregulated glial fibrillary acidic protein (GFAP) mRNA expression, and significantly increased proinflammatory factor mRNA/protein expression and release. The secretory capacity of astrocytes was reduced, with significantly decreased proinflammatory factor levels in culture supernatant after P-gp inhibitor verapamil pretreatment. The increase in the intracellular H2S level inhibited LPS-induced GFAP expression and P65 nuclear entry in astrocytes. mRNA expression and release of proinflammatory factors were reduced significantly, with no significant changes in cytoplasmic protein expression. S-sulfhydration levels increased significantly with the increased concentration of sodium hydrosulfide or S-adenosyl-l-methionine addition, with only moderate changes in astrocyte P-gp expression. H2S regulates NF-κB activation, leads to S-sulfhydration of P-gp, and inhibits the biosynthesis and secretion of proinflammatory factors by astrocytes. The regulatory effects of H2S on astrocytes may have clinical value for exploring new therapeutic strategies against neurodegenerative diseases.
Collapse
|
13
|
Mora Lagares L, Novič M. Recent Advances on P-Glycoprotein (ABCB1) Transporter Modelling with In Silico Methods. Int J Mol Sci 2022; 23:ijms232314804. [PMID: 36499131 PMCID: PMC9740644 DOI: 10.3390/ijms232314804] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/14/2022] [Accepted: 11/24/2022] [Indexed: 12/02/2022] Open
Abstract
ABC transporters play a critical role in both drug bioavailability and toxicity, and with the discovery of the P-glycoprotein (P-gp), this became even more evident, as it plays an important role in preventing intracellular accumulation of toxic compounds. Over the past 30 years, intensive studies have been conducted to find new therapeutic molecules to reverse the phenomenon of multidrug resistance (MDR) ), that research has found is often associated with overexpression of P-gp, the most extensively studied drug efflux transporter; in MDR, therapeutic drugs are prevented from reaching their targets due to active efflux from the cell. The development of P-gp inhibitors is recognized as a good way to reverse this type of MDR, which has been the subject of extensive studies over the past few decades. Despite the progress made, no effective P-gp inhibitors to reverse multidrug resistance are yet on the market, mainly because of their toxic effects. Computational studies can accelerate this process, and in silico models such as QSAR models that predict the activity of compounds associated with P-gp (or analogous transporters) are of great value in the early stages of drug development, along with molecular modelling methods, which provide a way to explain how these molecules interact with the ABC transporter. This review highlights recent advances in computational P-gp research, spanning the last five years to 2022. Particular attention is given to the use of machine-learning approaches, drug-transporter interactions, and recent discoveries of potential P-gp inhibitors that could act as modulators of multidrug resistance.
Collapse
Affiliation(s)
- Liadys Mora Lagares
- Correspondence: (L.M.L.); (M.N.); Tel.: +386-1-4760-438 (L.M.L.); +386-1-4760-253 (M.N.)
| | - Marjana Novič
- Correspondence: (L.M.L.); (M.N.); Tel.: +386-1-4760-438 (L.M.L.); +386-1-4760-253 (M.N.)
| |
Collapse
|
14
|
Ma BB, Montgomery AP, Chen B, Kassiou M, Danon JJ. Strategies for targeting the P2Y 12 receptor in the central nervous system. Bioorg Med Chem Lett 2022; 71:128837. [PMID: 35640763 DOI: 10.1016/j.bmcl.2022.128837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/10/2022] [Accepted: 05/26/2022] [Indexed: 11/28/2022]
Abstract
The purinergic 2Y type 12 receptor (P2Y12R) is a well-known biological target for anti-thrombotic drugs due to its role in platelet aggregation and blood clotting. While the importance of the P2Y12R in the periphery has been known for decades, much less is known about its expression and roles in the central nervous system (CNS), where it is expressed exclusively on microglia - the first responders to brain insults and neurodegeneration. Several seminal studies have shown that P2Y12 is a robust, translatable biomarker for anti-inflammatory and neuroprotective microglial phenotypes in models of degenerative diseases such as multiple sclerosis and Alzheimer's disease. An enduring problem for studying this receptor in vivo, however, is the lack of selective, high-affinity small molecule ligands that can bypass the blood-brain barrier and accumulate in the CNS. In this Digest, we discuss previous attempts by researchers to target the P2Y12R in the CNS and opine on strategies that may be employed to design and assess the suitability of novel P2Y12 ligands for this purpose going forward.
Collapse
Affiliation(s)
- Ben B Ma
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | | | - Biling Chen
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | - Michael Kassiou
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | - Jonathan J Danon
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia.
| |
Collapse
|
15
|
Breuil L, Goutal S, Marie S, Del Vecchio A, Audisio D, Soyer A, Goislard M, Saba W, Tournier N, Caillé F. Comparison of the Blood-Brain Barrier Transport and Vulnerability to P-Glycoprotein-Mediated Drug-Drug Interaction of Domperidone versus Metoclopramide Assessed Using In Vitro Assay and PET Imaging. Pharmaceutics 2022; 14:pharmaceutics14081658. [PMID: 36015284 PMCID: PMC9412994 DOI: 10.3390/pharmaceutics14081658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/01/2022] [Accepted: 08/05/2022] [Indexed: 11/16/2022] Open
Abstract
Domperidone and metoclopramide are widely prescribed antiemetic drugs with distinct neurological side effects. The impact of P-glycoprotein (P-gp)-mediated efflux at the blood−brain barrier (BBB) on brain exposure and BBB permeation was compared in vitro and in vivo using positron emission tomography (PET) imaging in rats with the radiolabeled analogs [11C]domperidone and [11C]metoclopramide. In P-gp-overexpressing cells, the IC50 of tariquidar, a potent P-gp inhibitor, was drastically different using [11C]domperidone (221 nM [198−248 nM]) or [11C]metoclopramide (4 nM [2−8 nM]) as the substrate. Complete P-gp inhibition led to a 1.8-fold higher increase in the cellular uptake of [11C]domperidone compared with [11C]metoclopramide (p < 0.0001). Brain PET imaging revealed that the baseline brain exposure (AUCbrain) of [11C]metoclopramide was 2.4-fold higher compared with [11C]domperidone (p < 0.001), consistent with a 1.8-fold higher BBB penetration (AUCbrain/AUCplasma). The maximal increase in the brain exposure (2.9-fold, p < 0.0001) and BBB penetration (2.9-fold, p < 0.0001) of [11C]metoclopramide was achieved using 8 mg/kg of tariquidar. In comparison, neither 8 nor 15 mg/kg of tariquidar increased the brain exposure of [11C]domperidone (p > 0.05). Domperidone is an avid P-gp substrate that was in vitro compared with metoclopramide. Domperidone benefits from a lower brain exposure and a limited risk for P-gp-mediated drug−drug interaction involving P-gp inhibition at the BBB.
Collapse
Affiliation(s)
- Louise Breuil
- Laboratoire d’Imagerie Biomédicale Multimodale (BIOMAPS), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, 4 place du Général Leclerc, 91401 Orsay, France
- Pharmacy Department, Robert-Debré Hospital, AP-HP, Université Paris Cité, 75019 Paris, France
| | - Sébastien Goutal
- Laboratoire d’Imagerie Biomédicale Multimodale (BIOMAPS), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, 4 place du Général Leclerc, 91401 Orsay, France
| | - Solène Marie
- Laboratoire d’Imagerie Biomédicale Multimodale (BIOMAPS), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, 4 place du Général Leclerc, 91401 Orsay, France
- Pharmacy Department, Bicêtre Hospital, AP-HP, Université Paris-Saclay, 94270 Le Kremlin-Bicêtre, France
| | - Antonio Del Vecchio
- CEA, Département Médicaments et Technologies pour la Santé, SCBM, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Davide Audisio
- CEA, Département Médicaments et Technologies pour la Santé, SCBM, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Amélie Soyer
- Laboratoire d’Imagerie Biomédicale Multimodale (BIOMAPS), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, 4 place du Général Leclerc, 91401 Orsay, France
| | - Maud Goislard
- Laboratoire d’Imagerie Biomédicale Multimodale (BIOMAPS), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, 4 place du Général Leclerc, 91401 Orsay, France
| | - Wadad Saba
- Laboratoire d’Imagerie Biomédicale Multimodale (BIOMAPS), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, 4 place du Général Leclerc, 91401 Orsay, France
| | - Nicolas Tournier
- Laboratoire d’Imagerie Biomédicale Multimodale (BIOMAPS), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, 4 place du Général Leclerc, 91401 Orsay, France
- Correspondence:
| | - Fabien Caillé
- Laboratoire d’Imagerie Biomédicale Multimodale (BIOMAPS), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, 4 place du Général Leclerc, 91401 Orsay, France
| |
Collapse
|
16
|
Jackson IM, Webb EW, Scott PJ, James ML. In Silico Approaches for Addressing Challenges in CNS Radiopharmaceutical Design. ACS Chem Neurosci 2022; 13:1675-1683. [PMID: 35606334 PMCID: PMC9945852 DOI: 10.1021/acschemneuro.2c00269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Positron emission tomography (PET) is a highly sensitive and versatile molecular imaging modality that leverages radiolabeled molecules, known as radiotracers, to interrogate biochemical processes such as metabolism, enzymatic activity, and receptor expression. The ability to probe specific molecular and cellular events longitudinally in a noninvasive manner makes PET imaging a particularly powerful technique for studying the central nervous system (CNS) in both health and disease. Unfortunately, developing and translating a single CNS PET tracer for clinical use is typically an extremely resource-intensive endeavor, often requiring synthesis and evaluation of numerous candidate molecules. While existing in vitro methods are beginning to address the challenge of derisking molecules prior to costly in vivo PET studies, most require a significant investment of resources and possess substantial limitations. In the context of CNS drug development, significant time and resources have been invested into the development and optimization of computational methods, particularly involving machine learning, to streamline the design of better CNS therapeutics. However, analogous efforts developed and validated for CNS radiotracer design are conspicuously limited. In this Perspective, we overview the requirements and challenges of CNS PET tracer design, survey the most promising computational methods for in silico CNS drug design, and bridge these two areas by discussing the potential applications and impact of computational design tools in CNS radiotracer design.
Collapse
Affiliation(s)
- Isaac M. Jackson
- Department of Radiology, Stanford University, Stanford, CA 94305
| | - E. William Webb
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109
| | - Peter J.H. Scott
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109;,Corresponding Authors: Peter J. H. Scott − Department of Radiology, University of Michigan, Ann Arbor, MI 48109, United States; , Michelle L. James − Departments of Radiology, and Neurology & Neurological Sciences, 1201 Welch Rd., P-206, Stanford, CA 94305-5484, United States;
| | - Michelle L. James
- Department of Radiology, Stanford University, Stanford, CA 94305;,Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA 94304.,Corresponding Authors: Peter J. H. Scott − Department of Radiology, University of Michigan, Ann Arbor, MI 48109, United States; , Michelle L. James − Departments of Radiology, and Neurology & Neurological Sciences, 1201 Welch Rd., P-206, Stanford, CA 94305-5484, United States;
| |
Collapse
|
17
|
Stéen EJL, Vugts DJ, Windhorst AD. The Application of in silico Methods for Prediction of Blood-Brain Barrier Permeability of Small Molecule PET Tracers. FRONTIERS IN NUCLEAR MEDICINE (LAUSANNE, SWITZERLAND) 2022; 2:853475. [PMID: 39354992 PMCID: PMC11440968 DOI: 10.3389/fnume.2022.853475] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/04/2022] [Indexed: 10/03/2024]
Abstract
Designing positron emission tomography (PET) tracers for targets in the central nervous system (CNS) is challenging. Besides showing high affinity and high selectivity for their intended target, these tracers have to be able to cross the blood-brain barrier (BBB). Since only a small fraction of small molecules is estimated to be able to cross the BBB, tools that can predict permeability at an early stage during the development are of great importance. One such tool is in silico models for predicting BBB-permeability. Thus far, such models have been built based on CNS drugs, with one exception. Herein, we sought to discuss and analyze if in silico predictions that have been built based on CNS drugs can be applied for CNS PET tracers as well, or if dedicated models are needed for the latter. Depending on what is taken into account in the prediction, i.e., passive diffusion or also active influx/efflux, there may be a need for a model build on CNS PET tracers. Following a brief introduction, an overview of a few selected in silico BBB-permeability predictions is provided along with a short historical background to the topic. In addition, a combination of previously reported CNS PET tracer datasets were assessed in a couple of selected models and guidelines for predicting BBB-permeability. The selected models were either predicting only passive diffusion or also the influence of ADME (absorption, distribution, metabolism and excretion) parameters. To conclude, we discuss the potential need of a prediction model dedicated for CNS PET tracers and present the key issues in respect to setting up a such a model.
Collapse
Affiliation(s)
- E Johanna L Stéen
- Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Danielle J Vugts
- Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Albert D Windhorst
- Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| |
Collapse
|
18
|
Quader S, Kataoka K, Cabral H. Nanomedicine for brain cancer. Adv Drug Deliv Rev 2022; 182:114115. [PMID: 35077821 DOI: 10.1016/j.addr.2022.114115] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 12/18/2021] [Accepted: 01/12/2022] [Indexed: 02/06/2023]
Abstract
CNS tumors remain among the deadliest forms of cancer, resisting conventional and new treatment approaches, with mortality rates staying practically unchanged over the past 30 years. One of the primary hurdles for treating these cancers is delivering drugs to the brain tumor site in therapeutic concentration, evading the blood-brain (tumor) barrier (BBB/BBTB). Supramolecular nanomedicines (NMs) are increasingly demonstrating noteworthy prospects for addressing these challenges utilizing their unique characteristics, such as improving the bioavailability of the payloadsviacontrolled pharmacokinetics and pharmacodynamics, BBB/BBTB crossing functions, superior distribution in the brain tumor site, and tumor-specific drug activation profiles. Here, we review NM-based brain tumor targeting approaches to demonstrate their applicability and translation potential from different perspectives. To this end, we provide a general overview of brain tumor and their treatments, the incidence of the BBB and BBTB, and their role on NM targeting, as well as the potential of NMs for promoting superior therapeutic effects. Additionally, we discuss critical issues of NMs and their clinical trials, aiming to bolster the potential clinical applications of NMs in treating these life-threatening diseases.
Collapse
Affiliation(s)
- Sabina Quader
- Innovation Center of NanoMedicine, Kawasaki Institute of Industrial Promotion, 3-25-14 Tonomachi, Kawasaki-ku, Kawasaki 212-0821, Japan
| | - Kazunori Kataoka
- Innovation Center of NanoMedicine, Kawasaki Institute of Industrial Promotion, 3-25-14 Tonomachi, Kawasaki-ku, Kawasaki 212-0821, Japan.
| | - Horacio Cabral
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
| |
Collapse
|
19
|
Pavan M, Bassani D, Bolcato G, Bissaro M, Sturles M, Moro S. Computational strategies to identify new drug candidates against neuroinflammation. Curr Med Chem 2022; 29:4756-4775. [PMID: 35135446 DOI: 10.2174/0929867329666220208095122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 11/22/2022]
Abstract
The even more increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier makeit mandatory to finely tune the candidates' physicochemical properties from the early stages of the discovery pipeline. The aim of this review is therefore to provide a general overview to the readers about the topic of neuroinflammation, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest. The techniques used to describe the molecular recognition mechanisms, such as molecular docking and molecular dynamics, will therefore be eviscerated, highlighting their advantages and their limitations. Finally, we report several case studies in which computational methods have been applied in drug discovery on neuroinflammation, focusing on the last decade's research.
Collapse
Affiliation(s)
- Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Giovanni Bolcato
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Mattia Sturles
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| |
Collapse
|
20
|
Drug Design Targeting the Muscarinic Receptors and the Implications in Central Nervous System Disorders. Biomedicines 2022; 10:biomedicines10020398. [PMID: 35203607 PMCID: PMC8962391 DOI: 10.3390/biomedicines10020398] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/24/2022] [Accepted: 01/26/2022] [Indexed: 11/16/2022] Open
Abstract
There is substantial evidence that cholinergic system function impairment plays a significant role in many central nervous system (CNS) disorders. During the past three decades, muscarinic receptors (mAChRs) have been implicated in various pathologies and have been prominent targets of drug-design efforts. However, due to the high sequence homology of the orthosteric binding site, many drug candidates resulted in limited clinical success. Although several advances in treating peripheral pathologies have been achieved, targeting CNS pathologies remains challenging for researchers. Nevertheless, significant progress has been made in recent years to develop functionally selective orthosteric and allosteric ligands targeting the mAChRs with limited side effect profiles. This review highlights past efforts and focuses on recent advances in drug design targeting these receptors for Alzheimer’s disease (AD), schizophrenia (SZ), and depression.
Collapse
|