1
|
Recent trends in pharmaceutical analysis to foster modern drug discovery by comparative in-silico profiling of drugs and related substances. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
2
|
Huang HJ, Lee YH, Chou CL, Zheng CM, Chiu HW. Investigation of potential descriptors of chemical compounds on prevention of nephrotoxicity via QSAR approach. Comput Struct Biotechnol J 2022; 20:1876-1884. [PMID: 35521549 PMCID: PMC9052077 DOI: 10.1016/j.csbj.2022.04.013] [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: 01/28/2022] [Revised: 04/02/2022] [Accepted: 04/11/2022] [Indexed: 11/15/2022] Open
Abstract
Drug-induced nephrotoxicity remains a common problem after exposure to medications and diagnostic agents, which may be heightened in the kidney microenvironment and deteriorate kidney function. In this study, the toxic effects of fourteen marked drugs with the individual chemical structure were evaluated in kidney cells. The quantitative structure-activity relationship (QSAR) approach was employed to investigate the potential structural descriptors of each drug-related to their toxic effects. The most reasonable equation of the QSAR model displayed that the estimated regression coefficients such as the number of ring assemblies, three-membered rings, and six-membered rings were strongly related to toxic effects on renal cells. Meanwhile, the chemical properties of the tested compounds including carbon atoms, bridge bonds, H-bond donors, negative atoms, and rotatable bonds were favored properties and promote the toxic effects on renal cells. Particularly, more numbers of rotatable bonds were positively correlated with strong toxic effects that displayed on the most toxic compound. The useful information discovered from our regression QSAR models may help to identify potential hazardous moiety to avoid nephrotoxicity in renal preventive medicine.
Collapse
Key Words
- AKI, acute kidney injury
- CKD, chronic kidney disease
- DIKD, drug-induced kidney disease
- ESRD, end‐stage renal disease
- GFA, genetic function approximation
- GFR, glomerular filtration rate
- Genetic algorithm
- KCSF, keratinocyte serum-free
- Nephrotoxicity
- PBS, phosphate buffered saline
- QSAR
- QSAR, quantitative structure-activity relationship
- SRB, sulforhodamine B
Collapse
Affiliation(s)
- Hung-Jin Huang
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yu-Hsuan Lee
- Department of Cosmeceutics, China Medical University, Taichung, Taiwan
| | - Chu-Lin Chou
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, Taiwan
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Hsin Kuo Min Hospital, Taipei Medical University, Taoyuan City, Taiwan
| | - Cai-Mei Zheng
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, Taiwan
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei, Taiwan
| | - Hui-Wen Chiu
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Medical Research, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| |
Collapse
|
3
|
Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback? Int J Mol Sci 2021; 22:ijms22105212. [PMID: 34069090 PMCID: PMC8156896 DOI: 10.3390/ijms22105212] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 01/01/2023] Open
Abstract
A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.
Collapse
|