In Silico Pharmacology for Evidence-Based and Precision Medicine

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Clinical Pharmaceutics".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 29653

Special Issue Editor


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Guest Editor
1. Department of Forensic Sciences and Toxicology, Faculty of Medicine, University of Crete, Heraklion, Greece
2. Computational BioMedicine Lab, Institute of Computer Science, Foundation for Research & Technology - Hellas, Heraklion, Greece
Interests: pharmacokinetics & pharmacodynamics; PBPK modeling and simulation; drug interactions; computational medicine; in silico pharmacology; pharmacometrics; computational oncology
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Special Issue Information

Dear Colleagues,

Personalized/precision medicine (PM), initiating from clinical and molecular pharmacology, introduces a new era in healthcare that aims to identify and predict the optimum treatments for a patient or a cohort. Evidence-based medicine (EBM) integrates information from basic in vivo, observational studies and clinical trials up to meta-analysis data for clinical consideration. Hence, it can be stated that EBM often see the forest (population averages) but misses the trees (individual patients), whereas the utilization of PM may not see the forest for the trees.

State-of-the-art tools for modeling and simulation (M&S) in pharmacology try to extrapolate knowledge gained through experimental procedures with either top-down or bottom-up approaches. M&S often try to “connect the dots” and reveal the bigger picture, the “population/forest”, considering what kind of “individuals/trees” are in there. These approaches have been providing state-of-the-art tools in research and development (R&D) for novel, more efficient and effective molecules, with improved safety profiles and the chance to proceed in clinical trials. In addition, they contribute to the R&D of novel drug-delivery systems and finally assist regarding drug repurposing.

This Special Issue invites research and review articles on pharmacological approaches incorporating M&S to promote EBM and PM knowledge for potential clinical extrapolation. Articles utilizing experimental or clinical data through M&S biomedical tools for disease progression dynamics, machine learning and relevant bioengineering approaches for drug response prediction (including drug interactions, adverse drug reactions and drug repurposing), PK/PD models for special population groups or novel drug delivery systems extrapolating in vitro/in vivo data to the clinical level are some of the (but not the only) research areas that this Special Issue aims to include.

Dr. Marios Spanakis
Guest Editor

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Keywords

  • personalized/precision medicine
  • evidence-based medicine approaches for therapy optimization
  • physiologically based PK/PD models
  • drug targeting and response prediction
  • multiscale M&S for disease dynamics
  • population pharmacokinetics/pharmacodynamics
  • in silico clinical trials
  • big data and systems pharmacology
  • M&S in clinical settings
  • translational and biomedical informatics in precision medicine

Published Papers (12 papers)

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Editorial

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4 pages, 194 KiB  
Editorial
In Silico Pharmacology for Evidence-Based and Precision Medicine
by Marios Spanakis
Pharmaceutics 2023, 15(3), 1014; https://doi.org/10.3390/pharmaceutics15031014 - 22 Mar 2023
Viewed by 1412
Abstract
Personalized/precision medicine (PM) originates from the application of molecular pharmacology in clinical practice, representing a new era in healthcare that aims to identify and predict optimum treatment outcomes for a patient or a cohort with similar genotype/phenotype characteristics [...] Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)

Research

Jump to: Editorial

27 pages, 7719 KiB  
Article
In Silico Drug Repurposing Framework Predicts Repaglinide, Agomelatine and Protokylol as TRPV1 Modulators with Analgesic Activity
by Corina Andrei, Dragos Paul Mihai, Anca Zanfirescu, George Mihai Nitulescu and Simona Negres
Pharmaceutics 2022, 14(12), 2563; https://doi.org/10.3390/pharmaceutics14122563 - 22 Nov 2022
Cited by 5 | Viewed by 1451
Abstract
Pain is one of the most common symptoms experienced by patients. The use of current analgesics is limited by low efficacy and important side effects. Transient receptor potential vanilloid-1 (TRPV1) is a non-selective cation channel, activated by capsaicin, heat, low pH or pro-inflammatory [...] Read more.
Pain is one of the most common symptoms experienced by patients. The use of current analgesics is limited by low efficacy and important side effects. Transient receptor potential vanilloid-1 (TRPV1) is a non-selective cation channel, activated by capsaicin, heat, low pH or pro-inflammatory agents. Since TRPV1 is a potential target for the development of novel analgesics due to its distribution and function, we aimed to develop an in silico drug repositioning framework to predict potential TRPV1 ligands among approved drugs as candidates for treating various types of pain. Structures of known TRPV1 agonists and antagonists were retrieved from ChEMBL databases and three datasets were established: agonists, antagonists and inactive molecules (pIC50 or pEC50 < 5 M). Structures of candidates for repurposing were retrieved from the DrugBank database. The curated active/inactive datasets were used to build and validate ligand-based predictive models using Bemis–Murcko structural scaffolds, plain ring systems, flexophore similarities and molecular descriptors. Further, molecular docking studies were performed on both active and inactive conformations of the TRPV1 channel to predict the binding affinities of repurposing candidates. Variables obtained from calculated scaffold-based activity scores, molecular descriptors criteria and molecular docking were used to build a multi-class neural network as an integrated machine learning algorithm to predict TRPV1 antagonists and agonists. The proposed predictive model had a higher accuracy for classifying TRPV1 agonists than antagonists, the ROC AUC values being 0.980 for predicting agonists, 0.972 for antagonists and 0.952 for inactive molecules. After screening the approved drugs with the validated algorithm, repaglinide (antidiabetic) and agomelatine (antidepressant) emerged as potential TRPV1 antagonists, and protokylol (bronchodilator) as an agonist. Further studies are required to confirm the predicted activity on TRPV1 and to assess the candidates’ efficacy in alleviating pain. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
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14 pages, 4636 KiB  
Article
New Data for Nebivolol after In Silico PK Study: Focus on Young Patients and Dosage Regimen
by Lara Marques, Bárbara Costa and Nuno Vale
Pharmaceutics 2022, 14(9), 1911; https://doi.org/10.3390/pharmaceutics14091911 - 09 Sep 2022
Cited by 2 | Viewed by 1875
Abstract
Nebivolol (NEB) is a highly selective β1 receptor antagonist with a distinct pharmacological profile. This drug is approved for the treatment of hypertension in the US, and hypertension and heart failure in Europe. Here, we review observations based on age dependence and explore [...] Read more.
Nebivolol (NEB) is a highly selective β1 receptor antagonist with a distinct pharmacological profile. This drug is approved for the treatment of hypertension in the US, and hypertension and heart failure in Europe. Here, we review observations based on age dependence and explore new drug regimens with in-silico studies, to achieve better efficacy and safety. The clinical data were obtained from six published literature reports. Then the data were used for model building, evaluation, and simulation. A two-compartment model with first-order absorption, lag time, linear elimination, and the following covariates: age and genotype were the ones best describing our population. Simulation of different dose regimens resulted in an increase chance of efficacy and safety when the dose regimen was altered to 6 mg every 36 h. It is worth noting that our population in this study constituted of young and healthy individuals. Studies regarding the effects of NEB according to age are scarce; however, they are needed to further improve efficacy and safety, and reduce adverse effects. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
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25 pages, 47903 KiB  
Article
Cheminformatics Identification of Phenolics as Modulators of Penicillin-Binding Protein 2a of Staphylococcus aureus: A Structure–Activity-Relationship-Based Study
by Jamiu Olaseni Aribisala and Saheed Sabiu
Pharmaceutics 2022, 14(9), 1818; https://doi.org/10.3390/pharmaceutics14091818 - 29 Aug 2022
Cited by 19 | Viewed by 2224
Abstract
The acquisition of penicillin-binding protein (PBP) 2a in resistant strains of Staphylococcus aureus allows for the continuous production of cell walls even after the inactivation of intrinsic PBPs. Thus, the discovery of novel therapeutics with enhanced modulatory activity on PBP2a is crucial, and [...] Read more.
The acquisition of penicillin-binding protein (PBP) 2a in resistant strains of Staphylococcus aureus allows for the continuous production of cell walls even after the inactivation of intrinsic PBPs. Thus, the discovery of novel therapeutics with enhanced modulatory activity on PBP2a is crucial, and plant secondary metabolites, such as phenolics, have found relevance in this regard. In this study, using computational techniques, phenolics were screened against the active site of PBP2a, and the ability of the lead phenolics to modulate PBP2a’s active and allosteric sites was studied. The top-five phenolics (leads) identified through structure–activity-based screening, pharmacokinetics and synthetic feasibility evaluations were subjected to molecular dynamics simulations. Except for propan-2-one at the active site, the leads had a higher binding free energy at both the active and allosteric sites of PBP2a than amoxicillin. The leads, while promoting the thermodynamic stability of PBP2a, showed a more promising affinity at the allosteric site than the active site, with silicristin (−25.61 kcal/mol) and epicatechin gallate (−47.65 kcal/mol) having the best affinity at the active and allosteric sites, respectively. Interestingly, the modulation of Tyr446, the active site gatekeeper residue in PBP2a, was noted to correlate with the affinity of the leads at the allosteric site. Overall, these observations point to the leads’ ability to inhibit PBP2a, either directly or through allosteric modulation with conventional drugs. Further confirmatory in vitro studies on the leads are underway. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
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21 pages, 4930 KiB  
Article
Revisiting Cerebrospinal Fluid Flow Direction and Rate in Physiologically Based Pharmacokinetic Model
by Makoto Hirasawa and Elizabeth C. M. de Lange
Pharmaceutics 2022, 14(9), 1764; https://doi.org/10.3390/pharmaceutics14091764 - 24 Aug 2022
Cited by 7 | Viewed by 3637
Abstract
The bidirectional pulsatile movement of cerebrospinal fluid (CSF), instead of the traditionally believed unidirectional and constant CSF circulation, has been demonstrated. In the present study, the structure and parameters of the CSF compartments were revisited in our comprehensive and validated central nervous system [...] Read more.
The bidirectional pulsatile movement of cerebrospinal fluid (CSF), instead of the traditionally believed unidirectional and constant CSF circulation, has been demonstrated. In the present study, the structure and parameters of the CSF compartments were revisited in our comprehensive and validated central nervous system (CNS)-specific, physiologically based pharmacokinetic (PBPK) model of healthy rats (LeiCNS-PK3.0). The bidirectional and site-dependent CSF movement was incorporated into LeiCNS-PK3.0 to create the new LeiCNS-PK“3.1” model. The physiological CSF movement rates in healthy rats that are unavailable from the literature were estimated by fitting the PK data of sucrose, a CSF flow marker, after intra-CSF administration. The capability of LeiCNS-PK3.1 to describe the PK profiles of other molecules was compared with that of the original LeiCNS-PK3.0 model. LeiCNS-PK3.1 demonstrated superior description of the CSF PK profiles of a range of small molecules after intra-CSF administration over LeiCNS-PK3.0. LeiCNS-PK3.1 also retained the same level of predictability of CSF PK profiles in cisterna magna after intravenous administration. These results support the theory of bidirectional and site-dependent CSF movement across the entire CSF space over unidirectional and constant CSF circulation in healthy rats, pointing out the need to revisit the structures and parameters of CSF compartments in CNS-PBPK models. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
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16 pages, 5851 KiB  
Article
Quality Control Dissolution Data Is Biopredictive for a Modified Release Ropinirole Formulation: Virtual Experiment with the Use of Re-Developed and Verified PBPK Model
by Olha Shuklinova, Przemysław Dorożyński, Piotr Kulinowski and Sebastian Polak
Pharmaceutics 2022, 14(7), 1514; https://doi.org/10.3390/pharmaceutics14071514 - 21 Jul 2022
Cited by 5 | Viewed by 1653
Abstract
Physiologically based pharmacokinetic and absorption modeling are being used by industry and regulatory bodies to address various scientifically challenging questions. While there is high confidence in the prediction of exposure for the BCS class I drugs administered as immediate-release formulations, in the case [...] Read more.
Physiologically based pharmacokinetic and absorption modeling are being used by industry and regulatory bodies to address various scientifically challenging questions. While there is high confidence in the prediction of exposure for the BCS class I drugs administered as immediate-release formulations, in the case of prolonged-release formulations, special attention should be given to the input dissolution data. Our goal was to develop and verify a PBPK model for a BCS class I compound, ropinirole, and check the biopredictiveness of the dissolution data for the prolonged-release formulation administered by Parkinson’s patients. The model was built based on quality control dissolution data reported in the certificates of analysis and verified with the use of data derived from five clinical trial reports. The simulated pharmacokinetic parameters being within a two-fold range of the observed values confirmed acceptable model performance, in vivo relevance of the in vitro dissolution profiles, and indirectly indicated ropinirole stable release from the formulation in the patients’ gastro-intestinal tract. Ropinirole PBPK model will be used for exploring potential clinical scenarios while developing a new formulation. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
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12 pages, 1686 KiB  
Article
Development of a Population Pharmacokinetic Model of Busulfan in Children and Evaluation of Different Sampling Schedules for Precision Dosing
by Efthymios Neroutsos, Ricardo Nalda-Molina, Anna Paisiou, Kalliopi Zisaki, Evgenios Goussetis, Alexandros Spyridonidis, Vasiliki Kitra, Stelios Grafakos, Georgia Valsami and Aristides Dokoumetzidis
Pharmaceutics 2022, 14(3), 647; https://doi.org/10.3390/pharmaceutics14030647 - 15 Mar 2022
Cited by 4 | Viewed by 2026
Abstract
We develop a population pharmacokinetic model to describe Busulfan pharmacokinetics in paediatric patients and investigate by simulations the impact of various sampling schedules on the calculation of AUC. Seventy-six children had 2 h infusions every 6 h. A two-compartment linear model was found [...] Read more.
We develop a population pharmacokinetic model to describe Busulfan pharmacokinetics in paediatric patients and investigate by simulations the impact of various sampling schedules on the calculation of AUC. Seventy-six children had 2 h infusions every 6 h. A two-compartment linear model was found to adequately describe the data. A lag-time was introduced to account for the delay of the administration of the drug through the infusion pump. The mean values of clearance, central volume of distribution, intercompartmental clearance, and peripheral volume of distribution were 10.7 L/h, 39.5 L, 4.68 L/h and 17.5 L, respectively, normalized for a Body Weight (BW) of 70 kg. BW was found to explain a portion of variability with an allometric relationship and fixed exponents of 0.75 on clearance parameters and 1 on volumes. Interindividual variability for clearance and volume of distribution was found to be 28% and 41%, respectively, and interoccasion variability for clearance was found to be 11%. Three sampling schedules were assessed by simulations for bias and imprecision to calculate AUC by a non-compartmental and a model-based method. The latter was found to be superior in all cases, while the non-compartmental was unbiased only in sampling up to 12 h corresponding to a once-daily dosing regimen. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
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18 pages, 1500 KiB  
Article
Physiologically Based Pharmacokinetic Modelling and Simulation to Predict the Plasma Concentration Profile of Doxorubicin
by George A. Mystridis, Georgios C. Batzias and Ioannis S. Vizirianakis
Pharmaceutics 2022, 14(3), 541; https://doi.org/10.3390/pharmaceutics14030541 - 28 Feb 2022
Cited by 1 | Viewed by 2343
Abstract
Doxorubicin (DOX) is still an important anticancer agent despite its tricky pharmacokinetics (PK) and toxicity potential. The advent of systems pharmacology enables the construction of PK models able to predict the concentration profiles of drugs and shed light on the underlying mechanisms involved [...] Read more.
Doxorubicin (DOX) is still an important anticancer agent despite its tricky pharmacokinetics (PK) and toxicity potential. The advent of systems pharmacology enables the construction of PK models able to predict the concentration profiles of drugs and shed light on the underlying mechanisms involved in PK and pharmacodynamics (PD). By utilizing existing published data and by analysing two clinical case studies we attempt to create physiologically based pharmacokinetic (PBPK) models for DOX using widely accepted methodologies. Based on two different approaches on three different key points we derived eight plausible models. The validation of the models provides evidence that is all performing as designed and opens the way for further exploitation by integrating metabolites and pharmacogenomic information. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
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19 pages, 356 KiB  
Article
Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach
by Kai Zhao, Yujia Shi and Hon-Cheong So
Pharmaceutics 2022, 14(2), 234; https://doi.org/10.3390/pharmaceutics14020234 - 20 Jan 2022
Cited by 5 | Viewed by 2492
Abstract
Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational approach [...] Read more.
Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational approach for drug target discovery based on machine learning (ML) models. ML models are first trained on drug-induced expression profiles with outcomes defined as whether the drug treats the studied disease. The goal is to “learn” the expression patterns associated with treatment. Then, the fitted ML models were applied to expression profiles from gene perturbations (overexpression (OE)/knockdown (KD)). We prioritized targets based on predicted probabilities from the ML model, which reflects treatment potential. The methodology was applied to predict targets for hypertension, diabetes mellitus (DM), rheumatoid arthritis (RA), and schizophrenia (SCZ). We validated our approach by evaluating whether the identified targets may ‘re-discover’ known drug targets from an external database (OpenTargets). Indeed, we found evidence of significant enrichment across all diseases under study. A further literature search revealed that many candidates were supported by previous studies. For example, we predicted PSMB8 inhibition to be associated with the treatment of RA, which was supported by a study showing that PSMB8 inhibitors (PR-957) ameliorated experimental RA in mice. In conclusion, we propose a new ML approach to integrate the expression profiles from drugs and gene perturbations and validated the framework. Our approach is flexible and may provide an independent source of information when prioritizing drug targets. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
16 pages, 5938 KiB  
Article
In Silico Evaluation of Binding of 2-Deoxy-D-Glucose with Mpro of nCoV to Combat COVID-19
by Anirudh Pratap Singh Raman, Kamlesh Kumari, Pallavi Jain, Vijay Kumar Vishvakarma, Ajay Kumar, Neha Kaushik, Eun Ha Choi, Nagendra Kumar Kaushik and Prashant Singh
Pharmaceutics 2022, 14(1), 135; https://doi.org/10.3390/pharmaceutics14010135 - 06 Jan 2022
Cited by 13 | Viewed by 2320
Abstract
COVID-19 has threatened the existence of humanity andthis infection occurs due to SARS-CoV-2 or novel coronavirus, was first reported in Wuhan, China. Therefore, there is a need to find a promising drug to cure the people suffering from the infection. The second wave [...] Read more.
COVID-19 has threatened the existence of humanity andthis infection occurs due to SARS-CoV-2 or novel coronavirus, was first reported in Wuhan, China. Therefore, there is a need to find a promising drug to cure the people suffering from the infection. The second wave of this viral infection was shaking the world in the first half of 2021. Drugs Controllers of India has allowed the emergency use of 2-deoxy-D-glucose (2DG) in 2021 for patients suffering from this viral infection. The potentiality of 2-deoxy-D-glucose to intervene in D-glucose metabolism exists and energy deprivation is an effective parameter to inhibit cancer cell development. Once 2DG arrives in the cells, it becomes phosphorylated to 2-deoxy-D-glucose-6-phosphate (2-DG6P), a charged molecule expressively captured inside the cells. On the other hand, 2DG lacks the ability to convert into fructose-6-phosphate, resulting in a hampering of the activity of both glucose-6-phosphate isomerase and hexokinase, and finally causing cell death. Hence, the potential and effectiveness of 2DG with the main protease (Mpro) of novel coronavirus (nCoV) should be investigated using the molecular docking and molecular dynamics (MD) simulations. The ability of 2DG to inhibit the Mpro of nCoV is compared with 2-deoxyglucose (2DAG), an acyclic molecule, and 2-deoxy-D-ribose (2DR). The binding energy of the molecules with the Mpro of nCoV is calculated using molecular docking and superimposed analysis data is obtained. The binding energy of 2DG, 2DR and 2DAG was −2.40, −2.22 and −2.88 kcal/mol respectively. Although the molecular docking does not provide reliable information, therefore, the binding affinity can be confirmed by molecular dynamics simulations. Various trajectories such as Rg, RMSD, RMSF, and hydrogen bonds are obtained from the molecular dynamics (MD) simulations. 2DG was found to be a better inhibitor than the 2DAG and 2DR based on the results obtained from the MD simulations at 300 K. Furthermore, temperature-dependent MD simulations of the Mpro of nCoV with promising 2DG was performed at 295, 310 and 315 K, and the effective binding with the Mpro of nCoV occurred at 295 K. With the use of DFT calculations, optimized geometry and localization of electron density of the frontier molecular orbitals were calculated. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
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21 pages, 7031 KiB  
Article
Multiomics Identification of Potential Targets for Alzheimer Disease and Antrocin as a Therapeutic Candidate
by Alexander T. H. Wu, Bashir Lawal, Li Wei, Ya-Ting Wen, David T. W. Tzeng and Wen-Cheng Lo
Pharmaceutics 2021, 13(10), 1555; https://doi.org/10.3390/pharmaceutics13101555 - 24 Sep 2021
Cited by 21 | Viewed by 3684
Abstract
Alzheimer’s disease (AD) is the most frequent cause of neurodegenerative dementia and affects nearly 50 million people worldwide. Early stage diagnosis of AD is challenging, and there is presently no effective treatment for AD. The specific genetic alterations and pathological mechanisms of the [...] Read more.
Alzheimer’s disease (AD) is the most frequent cause of neurodegenerative dementia and affects nearly 50 million people worldwide. Early stage diagnosis of AD is challenging, and there is presently no effective treatment for AD. The specific genetic alterations and pathological mechanisms of the development and progression of dementia remain poorly understood. Therefore, identifying essential genes and molecular pathways that are associated with this disease’s pathogenesis will help uncover potential treatments. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of AD, we integrated the differentially expressed genes (DEGs) from six microarray datasets of AD patients and controls. We identified ATPase H+ transporting V1 subunit A (ATP6V1A), BCL2 interacting protein 3 (BNIP3), calmodulin-dependent protein kinase IV (CAMK4), TOR signaling pathway regulator-like (TIPRL), and the translocase of outer mitochondrial membrane 70 (TOMM70) as upregulated DEGs common to the five datasets. Our analyses revealed that these genes exhibited brain-specific gene co-expression clustering with OPA1, ITFG1, OXCT1, ATP2A2, MAPK1, CDK14, MAP2K4, YWHAB, PARK2, CMAS, HSPA12A, and RGS17. Taking the mean relative expression levels of this geneset in different brain regions into account, we found that the frontal cortex (BA9) exhibited significantly (p < 0.05) higher expression levels of these DEGs, while the hippocampus exhibited the lowest levels. These DEGs are associated with mitochondrial dysfunction, inflammation processes, and various pathways involved in the pathogenesis of AD. Finally, our blood–brain barrier (BBB) predictions using the support vector machine (SVM) and LiCABEDS algorithm and molecular docking analysis suggested that antrocin is permeable to the BBB and exhibits robust ligand–receptor interactions with high binding affinities to CAMK4, TOMM70, and T1PRL. Our results also revealed good predictions for ADMET properties, drug-likeness, adherence to Lipinskís rules, and no alerts for pan-assay interference compounds (PAINS) Conclusions: These results suggest a new molecular signature for AD parthenogenesis and antrocin as a potential therapeutic agent. Further investigation is warranted. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
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17 pages, 2827 KiB  
Article
Zoledronic Acid as a Novel Dual Blocker of KIR6.1/2-SUR2 Subunits of ATP-Sensitive K+ Channels: Role in the Adverse Drug Reactions
by Fatima Maqoud, Rosa Scala, Vincenzo Tragni, Ciro Leonardo Pierri, Maria Grazia Perrone, Antonio Scilimati and Domenico Tricarico
Pharmaceutics 2021, 13(9), 1350; https://doi.org/10.3390/pharmaceutics13091350 - 27 Aug 2021
Cited by 12 | Viewed by 2075
Abstract
Zoledronic acid (ZOL) is used as a bone-specific antiresorptive drug with antimyeloma effects. Adverse drug reactions (A.D.R.) are associated with ZOL-therapy, whose mechanics are unknown. ZOL is a nitrogen-containing molecule whose structure shows similarities with nucleotides, ligands of ATP-sensitive K+ (KATP) channels. [...] Read more.
Zoledronic acid (ZOL) is used as a bone-specific antiresorptive drug with antimyeloma effects. Adverse drug reactions (A.D.R.) are associated with ZOL-therapy, whose mechanics are unknown. ZOL is a nitrogen-containing molecule whose structure shows similarities with nucleotides, ligands of ATP-sensitive K+ (KATP) channels. We investigated the action of ZOL by performing in vitro patch-clamp experiments on native KATP channels in murine skeletal muscle fibers, bone cells, and recombinant subunits in cell lines, and by in silico docking the nucleotide site on KIR and SUR, as well as the glibenclamide site. ZOL fully inhibited the KATP currents recorded in excised macro-patches from Extensor digitorum longus (EDL) and Soleus (SOL) muscle fibers with an IC50 of 1.2 ± 1.4 × 10−6 and 2.1 ± 3.7 × 10−10 M, respectively, and the KATP currents recorded in cell-attached patches from primary long bone cells with an IC50 of 1.6 ± 2.8 × 10−10 M. ZOL fully inhibited a whole-cell KATP channel current of recombinant KIR6.1-SUR2B and KIR6.2-SUR2A subunits expressed in HEK293 cells with an IC50 of 3.9 ± 2.7 × 10−10 M and 7.1 ± 3.1 × 10−6 M, respectively. The rank order of potency in inhibiting the KATP currents was: KIR6.1-SUR2B/SOL-KATP/osteoblast-KATP > KIR6.2-SUR2A/EDL-KATP >>> KIR6.2-SUR1 and KIR6.1-SUR1. Docking investigation revealed that the drug binds to the ADP/ATP sites on KIR6.1/2 and SUR2A/B and on the sulfonylureas site showing low binding energy <6 Kcal/mol for the KIR6.1/2-SUR2 subunits vs. the <4 Kcal/mol for the KIR6.2-SUR1. The IC50 of ZOL to inhibit the KIR6.1/2-SUR2A/B channels were correlated with its musculoskeletal and cardiovascular risks. We first showed that ZOL blocks at subnanomolar concentration musculoskeletal KATP channels and cardiac and vascular KIR6.2/1-SUR2 channels. Full article
(This article belongs to the Special Issue In Silico Pharmacology for Evidence-Based and Precision Medicine)
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