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Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model’s applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski’s rule. Molecular Docking performed on Wild Type, and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes’ stability allowing a comparative study on the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of darunavir and maybe used for further experimental studies. Our study may also be used as pipeline to search and design new potential inhibitors of HIV-1 proteases.

The battle against viral drug resistance highlights the need for innovative approaches to replace time-consuming and costly traditional methods. Deep generative models offer automation potential, especially in the fight against Human immunodeficiency virus (HIV), as they can synthesize diverse molecules effectively. In this paper, an application of an LSTM-based deep generative model named “LSTM-ProGen” is proposed to be tailored explicitly for the de novo design of drug candidate molecules that interact with a specific target protein (HIV-1 protease). LSTM-ProGen distinguishes itself by employing a long-short-term memory (LSTM) architecture, to generate novel molecules target specificity against the HIV-1 protease. Following a thorough training process involves fine-tuning LSTM-ProGen on a diverse range of compounds sourced from the ChEMBL database. The model was optimized to meet specific requirements, with multiple iterations to enhance its predictive capabilities and ensure it generates molecules that exhibit favorable target interactions. The training process encompasses an array of performance evaluation metrics, such as drug-likeness properties. Our evaluation includes extensive silico analysis using molecular docking and PCA-based visualization to explore the chemical space that the new molecules cover compared to those in the training set. These evaluations reveal that a subset of 12 de novo molecules generated by LSTM-ProGen exhibit a striking ability to interact with the target protein, rivaling or even surpassing the efficacy of native ligands. Extended versions with further refinement of LSTM-ProGen hold promise as versatile tools for designing efficacious and customized drug candidates tailored to specific targets, thus accelerating drug development and facilitating the discovery of new therapies for various diseases.

Since the first cases of AIDS, reported in 1980, this disease has become chronic over the years, and researchers have been trying to keep it under control. Despite the development and spread of mutate viruses, HIV protease remains an important pharmacological target. In the development of new HIV protease inhibitors, heterocyclic fragments have proven to be of great importance, owing to their rigid core structure, which may fit better into the enzyme’s hydrophobic pockets, and the presence of a heteroatom, which may increase the number of H-bonding interactions at the active site. According to the concept of targeting the protein backbone, different aromatic or non-aromatic heterocyclic moieties have yielded inhibitors with sufficient activity against mutant viruses. This paper provides an overview of HIV protease inhibitors developed over the last fifteen years, with a focus on the presence of heterocycles in their structure, either in the core or on the side chains, which are crucial for their activity. The rationale behind the design of these new inhibitors, as well as the key synthetic steps involved in their preparation, is also described.

Background. Numerous studies have shown that baseline drug resistance patterns may influence the outcome of antiretroviral therapy. Therefore, guidelines recommend drug resistance testing to guide the choice of initial regimen. In addition to optimizing individual patient management, these baseline resistance data enable transmitted drug resistance (TDR) to be surveyed for public health purposes. The SPREAD program systematically collects data to gain insight into TDR occurring in Europe since 2001. Methods. Demographic, clinical, and virological data from 4140 antiretroviral-naive human immunodeficiency virus (HIV)–infected individuals from 26 countries who were newly diagnosed between 2008 and 2010 were analyzed. Evidence of TDR was defined using the WHO list for surveillance of drug resistance mutations. Prevalence of TDR was assessed over time by comparing the results to SPREAD data from 2002 to 2007. Baseline susceptibility to antiretroviral drugs was predicted using the Stanford HIVdb program version 7.0. Results. The overall prevalence of TDR did not change significantly over time and was 8.3% (95% confidence interval, 7.2%–9.5%) in 2008–2010. The most frequent indicators of TDR were nucleoside reverse transcriptase inhibitor (NRTI) mutations (4.5%), follwed by nonnucleoside reverse transcriptase inhibitor (NNRTI) mutations (2.9%) and protease inhibitor mutations (2.0%). Baseline mutations were most predictive of reduced susceptibility to initial NNRTI-based regimens: 4.5% and 6.5% of patient isolates were predicted to have resistance to regimens containing efavirenz or rilpivirine, respectively, independent of current NRTI backbones. Conclusions. Although TDR was highest for NRTIs, the impact of baseline drug resistance patterns on susceptibility was largest for NNRTIs. The prevalence of TDR assessed by epidemiological surveys does not clearly indicate to what degree susceptibility to different drug classes is affected.

Siliciano and his colleagues propose a new index for measuring the antiviral activity of anti-HIV drugs in vitro , which suggests that there are limitations to the efficacy of antiviral drugs on the basis of their mechanism of action. They suggest that the new index is a more accurate way of measuring antiviral activity and that it correlates well with clinical outcomes. Highly active antiretroviral therapy (HAART) can control HIV-1 replication 1 , 2 , but suboptimal treatment allows for the evolution of resistance and rebound viremia 3 , 4 , 5 , 6 , 7 , 8 . A comparative measure of antiviral activity under clinically relevant conditions would guide drug development and the selection of regimens that maximally suppress replication. Here we show that current measures of antiviral activity, including IC 50 and inhibitory quotient, neglect a key dimension, the dose-response curve slope. Using infectivity assays with wide dynamic range, we show that this slope has noteworthy effects on antiviral activity. Slope values are class specific for antiviral drugs and define intrinsic limitations on antiviral activity for some classes. Nucleoside reverse transcriptase inhibitors and integrase inhibitors have slopes of ∼1, characteristic of noncooperative reactions, whereas non-nucleoside reverse transcriptase inhibitors, protease inhibitors and fusion inhibitors unexpectedly show slopes >1. Instantaneous inhibitory potential (IIP), the log reduction in single-round infectivity at clinical drug concentrations, is strongly influenced by slope and varies by >8 logs for anti-HIV drugs. IIP provides a more accurate measure of antiviral activity and in general correlates with clinical outcomes. Only agents with slopes >1 achieve high-level inhibition of single-round infectivity, a finding with profound implications for drug and vaccine development.

The AutoDock family of software has been widely used in protein-ligand docking research. This study compares AutoDock 4 and AutoDock Vina in the context of virtual screening by using these programs to select compounds active against HIV protease. Both programs were used to rank the members of two chemical libraries, each containing experimentally verified binders to HIV protease. In the case of the NCI Diversity Set II, both AutoDock 4 and Vina were able to select active compounds significantly better than random (AUC = 0.69 and 0.68, respectively; p<0.001). The binding energy predictions were highly correlated in this case, with r = 0.63 and iota = 0.82. For a set of larger, more flexible compounds from the Directory of Universal Decoys, the binding energy predictions were not correlated, and only Vina was able to rank compounds significantly better than random. In ranking smaller molecules with few rotatable bonds, AutoDock 4 and Vina were equally capable, though both exhibited a size-related bias in scoring. However, as Vina executes more quickly and is able to more accurately rank larger molecules, researchers should look to it first when undertaking a virtual screen.

Starting three decades ago and spreading rapidly around the world, acquired immunodeficiency syndrome (AIDS) is an infectious disease distinct from other contagious diseases by its unique ways of transmission. Over the past few decades, research into new drug compounds has been accompanied by extensive advances, and the design and manufacture of drugs that inhibit virus enzymes is one way to combat the AIDS virus. Since blocking enzyme activity can kill a pathogen or correct a metabolic imbalance, the design and use of enzyme inhibitors is a new approach against viruses. We carried out an in-depth analysis of the efficacy of atazanavir and its newly designed analogs as human immunodeficiency virus (HIV) protease inhibitors using molecular docking. The best-designed analogs were then compared with atazanavir by the molecular dynamics simulation. The most promising results were ultimately found based on the docking analysis for HIV protease. Several exhibited an estimated free binding energy lower than −9.45 kcal/mol, indicating better prediction results than the atazanavir. ATV7 inhibitor with antiviral action may be more beneficial for infected patients with HIV. Molecular dynamics analysis and binding energy also showed that the ATV7 drug had more inhibitory ability than the atazanavir drug. •Several new drugs have been developed based on atazanavir to inhibit HIV protease enzyme.•All interactions of AC1 inhibitor in the same condition by ATV have been investigated.•Results showed that the developed drug has better performance in HIV main protease inhibition.

The retroviral protease of human immunodeficiency virus (HIV) is an excellent target for antiviral inhibitors for treating HIV/AIDS. Despite the efficacy of therapy, current efforts to control the disease are undermined by the growing threat posed by drug resistance. This review covers the historical background of studies on the structure and function of HIV protease, the subsequent development of antiviral inhibitors, and recent studies on drug-resistant protease variants. We highlight the important contributions of Dr. Stephen Oroszlan to fundamental knowledge about the function of the HIV protease and other retroviral proteases. These studies, along with those of his colleagues, laid the foundations for the design of clinical inhibitors of HIV protease. The drug-resistant protease variants also provide an excellent model for investigating the molecular mechanisms and evolution of resistance.

Intestinal microbiome changes that occur in HIV positive individuals on different antiretroviral therapy (ART) regimens are important to understand, as they are potentially linked with chronic inflammation and microbiome-linked comorbidities that occur at increased incidence in this population. We conducted a cross-sectional study comparing the fecal microbiomes of HIV-uninfected (HIV SN) to HIV-infected individuals on long-term ART (HIV+ LTART) from Mexico using 16S ribosomal RNA (16sRNA) targeted sequencing. These individuals were on two ART regimens based on either Non-Nucleoside Reverse Transcriptase Inhibitors (EFV) or ritonavir-boosted Protease Inhibitors (PI) with the same backbone of Nucleoside Reverse Transcriptase Inhibitors. Microbiome diversity was reduced in treated HIV infection compared to HIV SN (p < 0.05). Several operational taxonomic units (OTUs) related to the Ruminococcaceae family including Faecalibacterium prausnitzii were depleted in EFV and PI compared to HIV SN and negatively correlated with intestinal gut dysfunction as measured by the intestinal fatty binding protein (p < 0.05). This is the first report to address the fecal bacterial communities in HIV-infected individuals on two ARV regimens from Mexico.

The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19–0–14–3 , 19–8–10–0 , and 19–8–14–3 , are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.