Shakoori Afnan, Alhindi Zain, Alobaidy Mohammad, Moulana Amna, Qashgari Ayman, Bagadood Rehab M, Sindi Ghadir, Atwah Banan, Khan Anmar Anwar
Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia.
Department of Anatomy, Faculty of Medicine, Umm Al-Qura University, Makkah P.O. Box 7607, Saudi Arabia.
J Vector Borne Dis. 2024 Oct 5. doi: 10.4103/JVBD.JVBD_65_24.
Quantum chemical & molecular docking practices to deliver new perceptions into how etoposide, novobiocin, nogalamycin and netropsin interact with the biological targets PF3D7_0918600 (Plasmodium falciparum 3D7). Further the pharmacokinetics of a drug candidate which influenced by a variety of factors, including P- glycoprotein (Pgp) transport, PBB (Plasma protein binding), & BBB (Blood-brain barrier) permeation help to forecast the pharmacological characteristics of acetyl-CoA reductase inhibitors (ADMEs) and their metabolites.
At this point, we have elevated four compounds such as etoposide, novobiocin, nogalamycin & netropsin. We have also studied molecular docking against the target protein of the Plasmodium falciparum (PF3D7_0918600) through exhausting the AutoDock Vina platform and AutoDock-Tools (ADT) and pharmacokinetic properties were carried out using the ADMET 2.0.
The relative results of molecular docking recommended a greater binding affinity of novobiocin with the selected receptors among other compounds. In-silico ADME screening is a computational approach utilised to forecast the pharmacological characteristics of acetyl- CoA reductase inhibitors (ADMEs) and their metabolites.
The ADMEs are based on the adsorption-desorption kinetics and pharmacopoeia. Adsorption and distribution analysis are used to assess the potential of the drug candidate. In vitro ADME is exploited to expect the effect of Pgp transport on the drug candidates. ADME has been used to predict CYP1A2 inhibitors and to predict PPB and BBB penetration. This paper summarizes the current knowledge on molecular docking, ADME and identifies potential drug candidates for ADME in vitro and in vivo.
量子化学和分子对接实践为依托泊苷、新生霉素、诺加霉素和纺锤菌素如何与生物靶点PF3D7_0918600（恶性疟原虫3D7）相互作用提供了新的认识。此外，受多种因素影响的候选药物的药代动力学，包括P-糖蛋白（Pgp）转运、血浆蛋白结合（PBB）和血脑屏障（BBB）渗透，有助于预测乙酰辅酶A还原酶抑制剂（ADME）及其代谢物的药理特性。
此时，我们提出了四种化合物，如依托泊苷、新生霉素、诺加霉素和纺锤菌素。我们还通过使用AutoDock Vina平台和AutoDock-Tools（ADT）对恶性疟原虫（PF3D7_0918600）的靶蛋白进行了分子对接研究，并使用ADMET 2.0进行了药代动力学性质研究。
分子对接的相关结果表明，与其他化合物相比，新生霉素与所选受体具有更高的结合亲和力。计算机辅助ADME筛选是一种用于预测乙酰辅酶A还原酶抑制剂（ADME）及其代谢物药理特性的计算方法。
ADME基于吸附-解吸动力学和药典。吸附和分布分析用于评估候选药物的潜力。体外ADME用于预测Pgp转运对候选药物的影响。ADME已被用于预测CYP1A2抑制剂以及预测PPB和BBB的渗透性。本文总结了目前关于分子对接、ADME的知识，并确定了体外和体内ADME的潜在候选药物。
