Dow College of Biotechnology, Department of Bioinformatics, Dow University of Health Sciences, Karachi, Pakistan.
International Centre for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.
Med Chem. 2022;18(2):288-305. doi: 10.2174/1573406417666210608143128.
New potential biological targets prediction through inverse molecular docking technique is another smart strategy to forecast the possibility of compounds being biologically active against various target receptors.
In this case of designed study, we screened our recently obtained novel acetylenic steroidal biotransformed products [(1) 8-β-methyl-14-α-hydroxyΔ4tibolone (2) 9-α-HydroxyΔ4 tibolone (3) 8-β-methyl-11-β-hydroxyΔ4tibolone (4) 6-β-hydroxyΔ4tibolone, (5) 6-β-9-α-dihydroxyΔ4tibolone (6) 7-β-hydroxyΔ4tibolone)] from fungi Cunninghemella Blakesleana to predict their possible biological targets and profiling of ADME properties.
The prediction of pharmacokinetic properties, membrane permeability, and bioavailability radar properties was carried out by using Swiss target prediction and Swiss ADME tools, respectively. These metabolites were also subjected to predict the possible mechanism of action along with associated biological network pathways by using Reactome database.
All the six screened compounds possessed excellent drug ability criteria and exhibited exceptionally excellent non-inhibitory potential against all five isozymes of the CYP450 enzyme complex, including CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4. All the screened compounds are lying within the acceptable pink zone of bioavailability radar and showing excellent descriptive properties. Compounds [1-4 & 6] are showing high BBB (Blood Brain Barrier) permeation, while compound 5 is exhibiting high HIA (Human Intestinal Absorption) property of (Egan Egg).
In conclusion, the results of this study smartly reveal that in-silico based studies are considered to provide robustness towards a rational drug design and development approach; therefore, in this way, it helps to avoid the possibility of failure of drug candidates in the later experimental stages of drug development phases.
通过反向分子对接技术预测新的潜在生物靶标是另一种预测化合物对各种靶受体具有生物活性的可能性的智能策略。
在本设计研究中，我们筛选了最近获得的新型炔基甾体生物转化产物[(1)8-β-甲基-14-α-羟基Δ4 替勃龙(2)9-α-羟基Δ4 替勃龙(3)8-β-甲基-11-β-羟基Δ4 替勃龙(4)6-β-羟基Δ4 替勃龙，(5)6-β-9-α-二羟基Δ4 替勃龙(6)7-β-羟基Δ4 替勃龙)]，以预测其可能的生物靶标，并分析 ADME 性质。
使用 Swiss Target Prediction 和 Swiss ADME 工具分别对这些代谢产物进行药代动力学性质、膜通透性和生物利用度雷达性质的预测。还使用 Reactome 数据库预测这些代谢产物可能的作用机制及其相关的生物网络途径。
所有筛选出的六种化合物均具有良好的药物能力标准，并对 CYP450 酶复合物的五种同工酶（包括 CYP1A2、CYP2C19、CYP2C9、CYP2D6 和 CYP3A4）均表现出极好的非抑制潜力。所有筛选出的化合物均位于生物利用度雷达的可接受粉红色区域内，表现出极好的描述性特征。化合物[1-4 和 6]显示出较高的 BBB（血脑屏障）渗透性，而化合物 5 则显示出较高的 HIA（人类肠道吸收）特性（Egan Egg）。
总之，本研究的结果表明，基于计算机的研究被认为是提供合理药物设计和开发方法的稳健性的一种方式；因此，通过这种方式，可以避免候选药物在药物开发后期实验阶段失败的可能性。
