A mechanistic and structural investigation of modified derivatives of the diaryltriazine class of NNRTIs targeting HIV-1 reverse transcriptase

N-Phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine as a New Class of HIV-1 Non-nucleoside Reverse Transcriptase Inhibitor

Highly active antiretroviral therapy (HAART) has revolutionized human immunodeficiency virus (HIV) healthcare, turning it from a terminal to a potentially chronic disease, although some patients can develop severe comorbidities. These include neurological complications, such as HIV-associated neurocognitive disorders (HAND), which result in cognitive and/or motor function symptoms. We now describe the discovery, synthesis, and evaluation of a new class of N-phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTI) aimed at avoiding HAND. The most promising molecule, 12126065, exhibited antiviral activity against wild-type HIV-1 in TZM cells (EC50 = 0.24 nM) with low in vitro cytotoxicity (CC50 = 4.8 μM) as well as retained activity against clinically relevant HIV mutants. 12126065 also demonstrated no in vivo acute or subacute toxicity, good in vivo brain penetration, and minimal neurotoxicity in mouse neurons up to 10 μM, with a 50% toxicity concentration (TC50) of >100 μM, well below its EC50.

Into the Fray! A Beginner's Guide to Medicinal Chemistry

Modern medicinal chemistry is a complex, multidimensional discipline that operates at the interface of the chemical and biological sciences. The medicinal chemistry contribution to drug discovery is typically described in the context of the well-recited linear progression of the drug discovery pipeline. However, compound optimization is idiosyncratic to each project, and clear definitions of hit and lead molecules and the subsequent progress along the pipeline becomes easily blurred. In addition, this description lacks insight into the entangled relationship between chemical and pharmacological properties, and thus provides limited guidance on how innovative medicinal chemistry strategies can be applied to solve optimization problems, regardless of the stage in the pipeline. Through discussion and illustrative examples, this article seeks to provide insights into the finesse of medicinal chemistry and the subtlety of balancing chemical properties pharmacology. In so doing, it aims to serve as an accessible and simple-to-digest guide for anyone who wishes to learn about the underlying principles of medicinal chemistry, in a context that has been decoupled from the pipeline description.

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.

The Journey of HIV-1 Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) from Lab to Clinic

Human immunodeficiency virus (HIV) infection is now pandemic. Targeting HIV-1 reverse transcriptase (HIV-1 RT) has been considered as one of the most successful targets for the development of anti-HIV treatment. Among the HIV-1 RT inhibitors, non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity, and low toxicity in antiretroviral combination therapies used to treat HIV. Until now, >50 structurally diverse classes of compounds have been reported as NNRTIs. Among them, six NNRTIs were approved for HIV-1 treatment, namely, nevirapine (NVP), delavirdine (DLV), efavirenz (EFV), etravirine (ETR), rilpivirine (RPV), and doravirine (DOR). In this perspective, we focus on the six NNRTIs and lessons learned from their journey through development to clinical studies. It demonstrates the obligatory need of understanding the physicochemical and biological principles (lead optimization), resistance mutations, synthesis, and clinical requirements for drugs.

Torsional flexibility of undecorated catechol diether compound as potent NNRTI targeting HIV-1 reverse transcriptase

Conformational adaptation of non-nucleoside reverse transcriptase inhibitor (NNRTI) via torsional flexibility is found to be very significant for targeting human immunodeficiency virus type 1 reverse transcriptase (HIV-1 RT) mutants. Catechol diether derivative including flexible torsions is new potent NNRTI with picomolar activity. Moreover, this derivative also reveals the good solubility, low toxicity and potent inhibition for HIV-1 mutants. In this study, torsional flexibility of an undecorated catechol diether compound in the binding pocket of wild type and mutants (Y181C and K103N/Y181C) HIV-1 RT is investigated by using QM/MM calculations. From the results, the uracil ring is found to exhibit more flexibility in the NNIBP. On the contrary, potential energy surfaces show that high energy is encountered by changing of the corresponding torsion of the cyanovinyl aryl ring indicating the limitation for torsional flexibility. For pointing out the key interaction for the binding, the residual interaction energies are performed by means of QM calculations. Important attractive interactions through hydrogen bonds between the inhibitor and K102, K/N103, V106, and Y188 are observed. The catechol ring is proposed to be modified in order to strengthen interactions with surrounding amino acids. The results may help for the designing of new potent NNRTIs.

Computer-aided discovery of anti-HIV agents

A review is provided on efforts in our laboratory over the last decade to discover anti-HIV agents. The work has focused on computer-aided design and synthesis of non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) with collaborative efforts on biological assaying and protein crystallography. Numerous design issues were successfully addressed including the need for potency against a wide range of viral variants, good aqueous solubility, and avoidance of electrophilic substructures. Computational methods including docking, de novo design, and free-energy perturbation (FEP) calculations made essential contributions. The result is novel NNRTIs with picomolar and low-nanomolar activities against wild-type HIV-1 and key variants that also show much improved solubility and lower cytotoxicity than recently approved drugs in the class.

Searching for novel scaffold of triazole non-nucleoside inhibitors of HIV-1 reverse transcriptase

Azoles are a promising class of the new generation of HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs). From thousands of reported compounds, many possess the same basic structure of an aryl substituted azole ring linked by a thioglycolamide chain with another aromatic ring. In order to find novel extensions for this basic scaffold, we explored the 5-position substitution pattern of triazole NNRTIs using molecular docking followed by the synthesis of selected compounds. We found that heterocyclic substituents in the 5-position of the triazole ring are detrimental to the inhibitory activity of compounds with four-membered thioglycolamide linker and this substitution seems to be viable only for compounds with shorter two-membered linker. Promising compound, N-(4-carboxy-2-chlorophenyl)-2-((4-benzyl-5-methyl-4H-1,2,4-triazol-3-yl)sulfanyl)acetamide, with potent inhibitory activity and acceptable aqueous solubility has been identified in this study that could serve as lead scaffold for the development of novel water-soluble salts of triazole NNRTIs.