Design and synthesis of highly potent HIV-1 protease inhibitors with novel isosorbide-derived P2 ligands

Incorporation of an Isohexide Subunit Improves the Drug-like Properties of Bioactive Compounds

An enhanced ability to pre-engineer favorable drug-likeness qualities into bioactive molecules would focus and streamline the drug development process. We find that phenols, carboxylic acids, and a purine react with isosorbide ("GRAS" designated) under Mitsunobu coupling conditions to deliver the isoidide conjugates selectively and efficiently. Such conjugates show improved solubility and permeability properties compared with the bare scaffold compounds themselves, and the purine adduct may have applications as a 2'-deoxyadenosine isostere. We anticipate additional benefits, implied by their structures, in metabolic stability and reduced toxicity of the isoidide conjugates as well.

Design, synthesis and biological evaluation of HIV-1 protease inhibitors with morpholine derivatives as P2 ligands in combination with cyclopropyl as P1' ligand

A series of novel HIV-1 protease inhibitors has been designed and synthesized, which contained morpholine derivatives as the P2 ligands and hydrophobic cyclopropyl as the P1' ligand at the meantime in this study, with the aim of improving the interactions between the active sites of HIV-1 protease and the inhibitors. Twenty-eight compounds were synthesized and assessed, among which inhibitors m18 and m1 exhibited excellent inhibitory effect on the activity of HIV-1 protease with IC₅₀ value of 47 nM and 53 nM, respectively. The molecular modeling of m1 revealed possible hydrogen bondings or van der Waals between the inhibitor and the protease, worthy of in-depth study.

Classification of HIV-1 Protease Inhibitors by Machine Learning Methods

HIV-1 protease plays an important role in the processing of virus infection. Protease is an effective therapeutic target for the treatment of HIV-1. Our data set is based on a selection of 4855 HIV-1 protease inhibitors (PIs) from ChEMBL. A series of 15 classification models for predicting the active inhibitors were built by machine learning methods, including k-nearest neighors (K-NN), decision tree (DT), random forest (RF), support vector machine (SVM), and deep neural network (DNN). The molecular structures were characterized by (1) fingerprint descriptors including MACCS fingerprints and PubChem fingerprints and (2) physicochemical descriptors calculated by CORINA Symphony. The prediction accuracies of all of the models are more than 70% on the test set; the best accuracy of 83.07% was obtained by model 4A, which was built by the SVM method based on MACCS fingerprint descriptors. Nine consensus models were built with three kinds of different descriptors, which combined all of the machine learning methods using the "consensus prediction". Model C3_a developed with MACCS fingerprint descriptors showed the highest accuracy on both training set (91.96%) and test set (83.15%). An external validation set including 35 989 compounds from DUD database and 239 active inhibitors from the recent literature was used to verify the performance of our model. The best prediction accuracy of 98.37% was obtained by model 3C, which was built by RF based on CORINA Symphony descriptors. In addition, from the analysis of molecular descriptors, it shows that the aromatic system and atoms related to hydrogen bonding provide important contributions to the bioactivity of PIs.

Recent Progress in the Development of HIV-1 Protease Inhibitors for the Treatment of HIV/AIDS

HIV-1 protease inhibitors continue to play an important role in the treatment of HIV/AIDS, transforming this deadly ailment into a more manageable chronic infection. Over the years, intensive research has led to a variety of approved protease inhibitors for the treatment of HIV/AIDS. In this review, we outline current drug design and medicinal chemistry efforts toward the development of next-generation protease inhibitors beyond the currently approved drugs.

Highly resistant HIV-1 proteases and strategies for their inhibition

The virally encoded protease is an important drug target for AIDS therapy. Despite the potency of the current drugs, infections with resistant viral strains limit the long-term effectiveness of therapy. Highly resistant variants of HIV protease from clinical isolates have different combinations of about 20 mutations and several orders of magnitude worse binding affinity for clinical inhibitors. Strategies are being explored to inhibit these highly resistant mutants. The existing inhibitors can be modified by introducing groups with the potential to form new interactions with conserved protease residues, and the flexible flaps. Alternative strategies are discussed, including designing inhibitors to bind to the open conformation of the protease dimer, and inhibition of the protease-catalyzed processing of the Gag-Pol precursor.