Design, synthesis and anti-HIV evaluation of novel diarylpyridine derivatives as potent HIV-1 NNRTIs

Deciphering the enigmas of non-nucleoside reverse transcriptase inhibitors (NNRTIs): A medicinal chemistry expedition towards combating HIV drug resistance

The pivotal involvement of reverse transcriptase activity in the pathogenesis of the progressive HIV virus has stimulated gradual advancements in drug discovery initiatives spanning three decades. Consequently, nonnucleoside reverse transcriptase inhibitors (NNRTIs) have emerged as a preeminent category of therapeutic agents for HIV management. Academic institutions and pharmaceutical companies have developed numerous NNRTIs, an essential component of antiretroviral therapy. Six NNRTIs have received Food and Drug Administration approval and are widely used in clinical practice, significantly improving the quality of HIV patients. However, the rapid emergence of drug resistance has limited the effectiveness of these medications, underscoring the necessity for perpetual research and development of novel therapeutic alternatives. To supplement the existing literatures on NNRTIs, a comprehensive review has been compiled to synthesize this extensive dataset into a comprehensible format for the medicinal chemistry community. In this review, a thorough investigation and meticulous analysis were conducted on the progressions achieved in NNRTIs within the past 8 years (2016-2023), and the experiences and insights gained in the development of inhibitors with varying chemical structures were also summarized. The provision of a crucial point of reference for the development of wide-ranging anti-HIV medications is anticipated.

Synthetic routes and structure-activity relationships (SAR) of anti-HIV agents: A key review

The worldwide increase of AIDS, an epidemic infection in constant development has an essential and still requires potent antiretroviral chemotherapeutic agents for reducing the integer of deaths caused by HIV. Thus, there is an urgent need for new anti-HIV drug candidates with increased strength, new targets, superior pharmacokinetic properties, and compact side effects. From this viewpoint, we first review present strategies of anti-HIV drug innovation and the synthesis of heterocyclic or natural compound as anti-HIV agents for facilitating the development of more influential and successful anti-HIV agents.

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of drugs approved were nucleoside reverse transcriptase inhibitors. A newer generation of reverse transcriptase inhibitors have become susceptible to drug resistant strains of HIV, and hence, alternatives are urgently needed. We have recently pioneered the use of Bayesian machine learning to generate models with public data to identify new compounds for testing against different disease targets. The current study has used the NIAID ChemDB HIV, Opportunistic Infection and Tuberculosis Therapeutics Database for machine learning studies. We curated and cleaned data from HIV-1 wild-type cell-based and reverse transcriptase (RT) DNA polymerase inhibition assays. Compounds from this database with ≤1 μM HIV-1 RT DNA polymerase activity inhibition and cell-based HIV-1 inhibition are correlated (Pearson r = 0.44, n = 1137, p < 0.0001). Models were trained using multiple machine learning approaches (Bernoulli Naive Bayes, AdaBoost Decision Tree, Random Forest, support vector classification, k-Nearest Neighbors, and deep neural networks as well as consensus approaches) and then their predictive abilities were compared. Our comparison of different machine learning methods demonstrated that support vector classification, deep learning, and a consensus were generally comparable and not significantly different from each other using 5-fold cross validation and using 24 training and test set combinations. This study demonstrates findings in line with our previous studies for various targets that training and testing with multiple data sets does not demonstrate a significant difference between support vector machine and deep neural networks.

Recent Advances in the Design and Development of Non-nucleoside Reverse Transcriptase Inhibitor Scaffolds

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have always been an important part of the anti-HIV-1 combination therapy known as combination antiretroviral therapy (cART) since 1996. The use of NNRTIs for about 22 years has led to some mutations in the residues that compose the reverse transcriptase active site, resulting in the emergence of drug-resistant viruses. Thus, the search for new potent NNRTIs with an improved safety profile and activity against drug-resistant HIV strains is indispensable, and many hit and lead NNRTIs have been discovered in the last decade. This review provides an overview of the development in this field from 2013 to August 2018.

Advances in diarylpyrimidines and related analogues as HIV-1 nonnucleoside reverse transcriptase inhibitors

HIV-1 nonnucleoside reverse transcriptase inhibitors (NNRTIs) have been playing an important role in the fight against acquired immunodeficiency syndrome (AIDS). Diarylpyrimidines (DAPYs) as the second generation NNRTIs, represented by etravirine (TMC125) and rilpivirine (TMC278), have attracted extensive attention due to their extraordinary potency, high specificity and low toxicity. However, the rapid emergence of drug-resistant virus strains and dissatisfactory pharmacokinetics of DAPYs present new challenges. In the past two decades, an increasing number of novel DAPY derivatives have emerged, which significantly enriched the structure-activity relationship of DAPYs. Studies of crystallography and molecular modeling have afforded a lot of useful information on structural requirements of NNRTIs, which contributes greatly to the improvement of their resistance profiles. In this review, we reviewed the discovery history and their evolution of DAPYs including their structural modification, derivatization and scaffold hopping in continuous pursuit of excellent anti-HIV drugs. And also, we discussed the prospect of DAPYs and the directions of future efforts.

Synthesis, biological evaluation and molecular modeling of novel azaspiro dihydrotriazines as influenza virus inhibitors targeting the host factor dihydrofolate reductase (DHFR)

Recently we identified cycloguanil-like dihydrotriazine derivatives, which provided host-factor directed antiviral activity against influenza viruses and respiratory syncytial virus (RSV), by targeting the human dihydrofolate reductase (hDHFR) enzyme. In this context we deemed interesting to further investigate the structure activity relationship (SAR) of our first series of cycloguanil-like dihydrotriazines, designing two novel azaspiro dihydrotriazine scaffolds. The present study allowed the exploration of the potential chemical space, around these new scaffolds, that are well tolerated for maintaining the antiviral effect by means of interaction with the hDHFR enzyme. The new derivatives confirmed their inhibitory profile against influenza viruses, especially type B. In particular, the two best compounds shared potent antiviral activity (4: EC₅₀ = 0.29 μM; 6: EC₅₀ = 0.19 μM), which was comparable to that of zanamivir (EC₅₀ = 0.14 μM), and better than that of ribavirin (EC₅₀ = 3.2 μM). In addition, these two compounds proved to be also effective against RSV (4: EC₅₀ = 0.40 μM, SI ≥ 250; 6: EC₅₀ = 1.8 μM, SI ≥ 56), surpassing the potency and selectivity index (SI) of ribavirin (EC₅₀ = 5.8 μM, SI > 43). By a perspective of these results, the above adequately substituted azaspiro dihydrotriazines may represent valuable hit compounds worthy of further structural optimization to develop improved host DHFR-directed antiviral agents.

In silico studies of diarylpyridine derivatives as novel HIV-1 NNRTIs using docking-based 3D-QSAR, molecular dynamics, and pharmacophore modeling approaches

A series of novel diarylpyridine derivatives has recently been identified as HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs), and most of them exhibited potent activities against HIV-1 strains, with EC₅₀ values in the low nanomolar range. However, the three-dimensional quantitative structure–activity relationships (3D-QSARs) and pharmacophore characteristics of these compounds remain to be studied. In the present study, forty-two diarylpyridine derivatives were firstly docked into HIV-1 reverse transcriptase, and molecular dynamics (10 ns) simulations were further performed to validate the reliability of the docking results, which indicated that residues Lys101, Tyr181, Tyr188, Phe227, and Trp229 might play important roles in binding with these diarylpyridines. The “U”-shaped docking conformations of all compounds were then used to construct 3D-QSAR and pharmacophore models. The satisfactory statistical parameters of CoMFA (q_loo² = 0.665, r_ncv² = 0.989, r_pred² = 0.962, etc.) and CoMSIA (q_loo² = 0.727, r_ncv² = 0.988, r_pred² = 0.912, etc.) models demonstrated that both constructed models had excellent predictability, and their contour maps gave insights into the structural requirements of the diarylpyridines for the anti-HIV-1 activity. A docking-conformation-based pharmacophore model, containing three hydrophobic centers, three hydrogen-bond acceptors, and three hydrogen-bond donors, was also established. The observations in this study might provide important information for the rational design and development of novel HIV-1 NNRTIs.

Computational modeling approaches were successfully applied to a series of diarylpyridine derivatives as novel HIV-1 NNRTIs.