The design of drugs for HIV and HCV

Organoselenium Compounds in Medicinal Chemistry

The chemical and biological interest in this element and the molecules bearing selenium has been exponentially growing over the years. Selenium, formerly designated as a toxin, becomes a vital trace element for life that appears as selenocysteine and its dimeric form, selenocystine, in the active sites of selenoproteins, which catalyze a wide variety of reactions, including the detoxification of reactive oxygen species and modulation of redox activities. From the point of view of drug developments, organoselenium drugs are isosteres of sulfur-containing and oxygen-containing drugs with the advantage that the presence of the selenium atom confers antioxidant properties and high lipophilicity, which would increase cell membrane permeation leading to better oral bioavailability. This statement is the paramount relevance considering the big number of clinically employed compounds bearing sulfur or oxygen atoms in their structures including nucleosides and carbohydrates. Thus, in this article we have focused on the relevant features of the application of selenium in medicinal chemistry. With the increasing interest in selenium chemistry, we have attempted to highlight the most significant published data on this subject, mainly concentrating the analysis on the last years. In consequence, the recent advances of relevant pharmacological organoselenium compounds are discussed.

Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks

Botanical formulations are promising candidates for developing new biopesticides that can protect crops from pests and diseases while reducing harm to the environment. These biopesticides can be combined with permeation enhancer compounds to boost their efficacy against pests and fungal diseases. However, finding synergistic combinations of these compounds is challenging due to the large and complex chemical space. In this paper, we propose a novel deep learning method that can predict the synergy of botanical products and permeation enhancers based on in vitro assay data. Our method uses a weighted combination of component feature vectors to represent the input mixtures, which enables the model to handle a variable number of components and to interpret the contribution of each component to the synergy. We also employ an ensemble of interpretation methods to provide insights into the underlying mechanisms of synergy. We validate our method by testing the predicted synergistic combinations in wet-lab experiments and show that our method can discover novel and effective biopesticides that would otherwise be difficult to find. Our method is generalizable and applicable to other domains, where predicting mixtures of chemical compounds is important.

Inhibitors of the Structural and Nonstructural Proteins of Alphaviruses

The Alphavirus genus includes viruses that cause encephalitis due to neuroinvasion and viruses that cause arthritis due to acute and chronic inflammation. There is no approved therapeutic for alphavirus infections, but significant efforts are ongoing, more so in recent years, to develop vaccines and therapeutics for alphavirus infections. This review article highlights some of the major advances made so far to identify small molecules that can selectively target the structural and the nonstructural proteins in alphaviruses with the expectation that persistent investigation of an increasingly expanding chemical space through a variety of structure-based design and high-throughput screening strategies will yield candidate drugs for clinical studies. While most of the works discussed are still in the early discovery to lead optimization stages, promising avenues remain for drug development against this family of viruses.

Fixing the Achilles Heel of Pfizer's Paxlovid for COVID-19 Treatment

Nirmatrelvir (PF-07321332), a first-in-class inhibitor of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) main protease (Mpro), was developed by Pfizer under intense pressure during the pandemic to treat COVID-19. A weakness of nirmatrelvir is its limited metabolic stability, which led to the development of a combination therapy (paxlovid), involving coadministration of nirmatrelvir with the cytochrome P450 inhibitor ritonavir. However, limitations in tolerability of the ritonavir component reduce the scope of paxlovid. In response to these limitations, researchers at Pfizer have now developed the second-generation Mpro inhibitor PF-07817883 (ibuzatrelvir). Structurally related to nirmatrelvir, including with the presence of a trifluoromethyl group, albeit located differently, ibuzatrelvir manifests enhanced oral bioavailability, so it does not require coadministration with ritonavir. The development of ibuzatrelvir is an important milestone, because it is expected to enhance the treatment of COVID-19 without the drawbacks associated with ritonavir. Given the success of paxlovid in treating COVID-19, it is likely that ibuzatrelvir will be granted approval as an improved drug for treatment of COVID-19 infections, so complementing vaccination efforts and improving pandemic preparedness. The development of nirmatrelvir and ibuzatrelvir dramatically highlights the power of appropriately resourced modern medicinal chemistry to very rapidly enable the development of breakthrough medicines. Consideration of how analogous approaches can be used to develop similarly breakthrough medicines for infectious diseases such as tuberculosis and malaria is worthwhile.

The Phenomenon of Antiretroviral Drug Resistance in the Context of Human Immunodeficiency Virus Treatment: Dynamic and Ever Evolving Subject Matter

Human immunodeficiency virus (HIV) is a significant global health issue that affects a substantial number of individuals across the globe, with a total of 39 million individuals living with HIV/AIDS. ART has resulted in a reduction in HIV-related mortality. Nevertheless, the issue of medication resistance is a significant obstacle in the management of HIV/AIDS. The unique genetic composition of HIV enables it to undergo rapid mutations and adapt, leading to the emergence of drug-resistant forms. The development of drug resistance can be attributed to various circumstances, including noncompliance with treatment regimens, insufficient dosage, interactions between drugs, viral mutations, preexposure prophylactics, and transmission from mother to child. It is therefore essential to comprehend the molecular components of HIV and the mechanisms of antiretroviral medications to devise efficacious treatment options for HIV/AIDS.

Synthetic Gene Circuits for Regulation of Next-Generation Cell-Based Therapeutics

Arming human cells with synthetic gene circuits enables to expand their capacity to execute superior sensing and response actions, offering tremendous potential for innovative cellular therapeutics. This can be achieved by assembling components from an ever-expanding molecular toolkit, incorporating switches based on transcriptional, translational, or post-translational control mechanisms. This review provides examples from the three classes of switches, and discusses their advantages and limitations to regulate the activity of therapeutic cells in vivo. Genetic switches designed to recognize internal disease-associated signals often encode intricate actuation programs that orchestrate a reduction in the sensed signal, establishing a closed-loop architecture. Conversely, switches engineered to detect external molecular or physical cues operate in an open-loop fashion, switching on or off upon signal exposure. The integration of such synthetic gene circuits into the next generation of chimeric antigen receptor T-cells is already enabling precise calibration of immune responses in terms of magnitude and timing, thereby improving the potency and safety of therapeutic cells. Furthermore, pre-clinical engineered cells targeting other chronic diseases are gathering increasing attention, and this review discusses the path forward for achieving clinical success. With synthetic biology at the forefront, cellular therapeutics holds great promise for groundbreaking treatments.

Vector-Transmitted Flaviviruses: An Antiviral Molecules Overview

Flaviviruses cause numerous pathologies in humans across a broad clinical spectrum with potentially severe clinical manifestations, including hemorrhagic and neurological disorders. Among human flaviviruses, some viral proteins show high conservation and are good candidates as targets for drug design. From an epidemiological point of view, flaviviruses cause more than 400 million cases of infection worldwide each year. In particular, the Yellow Fever, dengue, West Nile, and Zika viruses have high morbidity and mortality-about an estimated 20,000 deaths per year. As they depend on human vectors, they have expanded their geographical range in recent years due to altered climatic and social conditions. Despite these epidemiological and clinical premises, there are limited antiviral treatments for these infections. In this review, we describe the major compounds that are currently under evaluation for the treatment of flavivirus infections and the challenges faced during clinical trials, outlining their mechanisms of action in order to present an overview of ongoing studies. According to our review, the absence of approved antivirals for flaviviruses led to in vitro and in vivo experiments aimed at identifying compounds that can interfere with one or more viral cycle steps. Still, the currently unavailability of approved antivirals poses a significant public health issue.

4'-Ethynyl-2'-deoxy-2'-β-fluoro-2-fluoroadenosine: A Highly Potent and Orally Available Clinical Candidate for the Treatment of HIV-1 Infection

2'-Deoxy-2'-β-fluoroadenosines bearing 4'-azido or 4'-ethynyl groups designed for the treatment of HIV-1 infection have been synthesized. All these compounds possess nanomolar anti-HIV-1 activity, with the 4'-ethynyl-2-fluoroadenosine analog 1c (CL-197) being the most potent compound with low cytotoxicity (EC50 = 0.9 nM, CC50 > 100 μM). It also shows potent inhibitory activities on drug resistant and clinical HIV-1 strains. Oral administration of 1c to Beagle dogs resulted in high levels of its bioactive form 1c-TP in peripheral blood mononuclear cells, the HIV-1 target cells, where the resulting triphosphate exhibited a long-term intracellular retention and could prevent HIV-1 infection for an extended time. 1c displayed low in vivo toxicity and favorable pharmacokinetics profiles in Sprague-Dawley rats. The preclinical data support further development of 1c as a highly potent and orally bioavailable clinical candidate to treat HIV-1 infection. Currently, CL-197 is in clinical trials in China (registration number: CXHL2200529).

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

An under-explored target for SARS-CoV-2 is the S-adenosyl methionine (SAM)-dependent methyltransferase Nsp14, which methylates the N7-guanosine of viral RNA at the 5'-end, allowing the virus to evade host immune response. We sought new Nsp14 inhibitors with three large library docking strategies. First, up to 1.1 billion lead-like molecules were docked against the enzyme's SAM site, leading to three inhibitors with IC50 values from 6 to 50 μM. Second, docking a library of 16 million fragments revealed 9 new inhibitors with IC50 values from 12 to 341 μM. Third, docking a library of 25 million electrophiles to covalently modify Cys387 revealed 7 inhibitors with IC50 values from 3.5 to 39 μM. Overall, 32 inhibitors encompassing 11 chemotypes had IC50 values < 50 μM and 5 inhibitors in 4 chemotypes had IC50 values < 10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing starting points for future optimization.

Discovery of non-nucleoside oxindole derivatives as potent inhibitors against dengue RNA-dependent RNA polymerase

A series of thiazole linked Oxindole-5-Sulfonamide (OSA) derivatives were designed as inhibitors of RNA-dependent RNA polymerase (RdRp) activity of Dengue virus. These were synthesized and then evaluated for their efficacy in ex-vivo virus replication assay using human cell lines. Among 20 primary compounds in the series, OSA-15 was identified as a hit. A series of analogues were synthesized by replacing the difluoro benzyl group of OSA-15 with different substituted benzyl groups. The efficacy of OSA-15derivatives was less than that of the parent compound, except OSA-15-17, which has shown improved efficacy than OSA-15. The further optimization was carried out by adding dimethyl (DM) groups to both the sulfonamide and oxindole NH's to produce OSA-15-DM and OSA-15-17-DM. These two compounds were showing no detectable cytotoxicity and the latter was more efficacious. Further, both these compounds were tested for inhibition in all the serotypes of the Dengue virus using an ex-vivo assay. The EC₅₀ of OSA-15-17-DM was observed in a low micromolar range between 2.5 and 5.0 µg/ml. Computation docking and molecular dynamics simulation studies confirmed the binding of identified hits to DENV RdRp. OSA15-17-DM blocks the RNA entrance and elongation site for their biological activity with high binding affinity. Overall, the identified oxindole derivatives are novel compounds that can inhibit Dengue replication, working as non-nucleoside inhibitors (NNI) to explore as anti-viral RdRp activity.

Drug Combination Modeling: Methods and Applications in Drug Development

Combination therapies have become increasingly researched and used in the treatment and management of complex diseases due to their ability to increase the chances for better efficacy and decreased toxicity. To evaluate drug combinations in drug development, pharmacokinetic and pharmacodynamic interactions between drugs in combination can be quantified using mathematical models; however, it can be difficult to deduce which models to use and how to use them to aid in clinical trial simulations to simulate the effect of a drug combination. This review paper aims to provide an overview of the various methods used to evaluate combination drug interaction for use in clinical trial development and a practical guideline on how combination modeling can be used in the settings of clinical trials.

R2-DDI: relation-aware feature refinement for drug-drug interaction prediction

Precisely predicting the drug-drug interaction (DDI) is an important application and host research topic in drug discovery, especially for avoiding the adverse effect when using drug combination treatment for patients. Nowadays, machine learning and deep learning methods have achieved great success in DDI prediction. However, we notice that most of the works ignore the importance of the relation type when building the DDI prediction models. In this work, we propose a novel R$^2$-DDI framework, which introduces a relation-aware feature refinement module for drug representation learning. The relation feature is integrated into drug representation and refined in the framework. With the refinement features, we also incorporate the consistency training method to regularize the multi-branch predictions for better generalization. Through extensive experiments and studies, we demonstrate our R$^2$-DDI approach can significantly improve the DDI prediction performance over multiple real-world datasets and settings, and our method shows better generalization ability with the help of the feature refinement design.

SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction

Synergistic drug combinations can improve the therapeutic effect and reduce the drug dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen synergistic drug combinations. However, the in vitro method is time-consuming and expensive. With the rapid growth of high-throughput data, computational methods are becoming efficient tools to predict potential synergistic drug combinations. Considering the limitations of the previous computational methods, we developed a new model named Siamese Network and Random Matrix Projection for AntiCancer Drug Combination prediction (SNRMPACDC). Firstly, the Siamese convolutional network and random matrix projection were used to process the features of the two drugs into drug combination features. Then, the features of the cancer cell line were processed through the convolutional network. Finally, the processed features were integrated and input into the multi-layer perceptron network to get the predicted score. Compared with the traditional method of splicing drug features into drug combination features, SNRMPACDC improved the interpretability of drug combination features to a certain extent. In addition, the introduction of convolutional networks can better extract the potential information in the features. SNRMPACDC achieved the root mean-squared error of 15.01 and the Pearson correlation coefficient of 0.75 in 5-fold cross-validation of regression prediction for response data. In addition, SNRMPACDC achieved the AUC of 0.91 ± 0.03 and the AUPR of 0.62 ± 0.05 in 5-fold cross-validation of classification prediction of synergistic or not. These results are almost better than all the previous models. SNRMPACDC would be an effective approach to infer potential anticancer synergistic drug combinations.

Structural and Functional Characterization of Indane-Core CD4-Mimetic Compounds Substituted with Heterocyclic Amines

The human immunodeficiency virus (HIV-1) envelope glycoprotein (Env) trimer on the virion surface interacts with the host receptors, CD4 and CCR5/CXCR4, to mediate virus entry into the target cell. CD4-mimetic compounds (CD4mcs) bind the gp120 Env, block CD4 binding, and inactivate Env. Previous studies suggested that a C(5)-methylamino methyl moiety on a lead CD4mc, BNM-III-170, contributed to its antiviral potency. By replacing the C(5) chain with differentially substituted pyrrolidine, piperidine, and piperazine ring systems, guided by structural and computational analyses, we found that the 5-position of BNM-III-170 is remarkably tolerant of a variety of ring sizes and substitutions, both in regard to antiviral activity and sensitization to humoral responses. Crystallographic analyses of representative analogues from the pyrrolidine series revealed the potential for 5-substituents to hydrogen bond with gp120 Env residue Thr 283. Further optimization of these interactions holds promise for the development of CD4mcs with greater potency.

The novel tetrahydropyrimidine derivative as inhibitor of SARS CoV-2: synthesis, modeling and molecular docking analysis

N-(1,3-Benzothiazol-2-yl)-N-(1,4,5,6-tetrahydro-1H-pyrimidine-2-yl) amine was synthesized and characterized by elemental analysis, FT-IR, NMR and X-ray single crystal diffraction. The compound structure belongs to the triclinic system with the P-1 space group with unit cell parameters a = 11.9290(4), b = 13.2547(4) and c = 15.3904(5) Å. Hirhsfeld surface analysis is performed to revealintermolecular interactions with these interactions. The molecular structure, vibrational spectroscopic data and HOMOs and LUMOs analyses were calculated by using the DFT/B3LYP method with the 6-311 + G(d,p)) basis set. Some of pharmacokinetic parameters and drug-likeness properties of the compound were also performed. Besides these, the present work is a searching to test N-(1,3-benzothiazol-2-yl)-N-(1,4,5,6-tetrahydro-1H-pyrimidine-2-yl) amine as an inhibitor for the SARS-CoV-2. For this aim, the molecular docking analysis of the synthesized compound was applied along with Favipiravir. Besides the docking results, ADMET properties of the compound were also calculated.Communicated by Ramaswamy H. Sarma.

5-Halogenation of Uridine Suppresses Protonation-Induced Tautomerization and Enhances Glycosidic Bond Stability of Protonated Uridine: Investigations via IRMPD Action Spectroscopy, ER-CID Experiments, and Theoretical Calculations

Uridine (Urd), a canonical nucleoside of RNA, is the most commonly modified nucleoside among those that occur naturally. Uridine has also been an important target for the development of modified nucleoside analogues for pharmaceutical applications. In this work, the effects of 5-halogenation of uracil on the structures and glycosidic bond stabilities of protonated uridine nucleoside analogues are examined using tandem mass spectrometry and computational methods. Infrared multiple photon dissociation (IRMPD) action spectroscopy experiments and theoretical calculations are performed to probe the structural influences of these modifications. Energy-resolved collision-induced dissociation experiments along with survival yield analyses are performed to probe glycosidic bond stability. The measured IRMPD spectra are compared to linear IR spectra predicted for the stable low-energy conformations of these species computed at the B3LYP/6-311+G(d,p) level of theory to determine the conformations experimentally populated. Spectral signatures in the IR fingerprint and hydrogen-stretching regions allow the 2,4-dihydroxy protonated tautomers (T) and O4- and O2-protonated conformers to be readily differentiated. Comparisons between the measured and predicted spectra indicate that parallel to findings for uridine, both T and O4-protonated conformers of the 5-halouridine nucleoside analogues are populated, whereas O2-protonated conformers are not. Variations in yields of the spectral signatures characteristic of the T and O4-protonated conformers indicate that the extent of protonation-induced tautomerization is suppressed as the size of the halogen substituent increases. Trends in the energy-dependence of the survival yield curves find that 5-halogenation strengthens the glycosidic bond and that the enhancement in stability increases with the size of the halogen substituent.

Ganoderma microsporum immunomodulatory protein acts as a multifunctional broad-spectrum antiviral against SARS-CoV-2 by interfering virus binding to the host cells and spike-mediated cell fusion

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible coronavirus that has caused over 6 million fatalities. SARS-CoV-2 variants with spike mutations are frequently endowed with a strong capability to escape vaccine-elicited protection. Due to this characteristic, a broad-spectrum inhibitor against SARS-CoV-2 infection is urgently demanded. Ganoderma microsporum immunomodulatory protein (GMI) was previously reported to alleviate infection of SARS-CoV-2 through ACE2 downregulation whereas the impact of GMI on virus itself was less understood. Our study aims to determine the effects of GMI on SARS-CoV-2 pseudovirus and the more detailed mechanisms of GMI inhibition against SARS-CoV-2 pseudovirus infection.

METHODS

ACE2-overexpressing HEK293T cells (HEK293T/ACE2) and SARS-CoV-2 pseudoviruses carrying spike variants were used to study the effects of GMI in vitro. Infectivity was evaluated by fluorescence microscopy and flow cytometry. Fusion rate mediated by SARS-CoV-2 spike protein was examined with split fluorescent protein /luciferase systems. The interactions of GMI with SARS-CoV-2 pseudovirus and ACE2 were investigated by immunoprecipitation and immunoblotting.

RESULTS

GMI broadly blocked SARS-CoV-2 infection in various cell lines. GMI effectively inhibited the infection of pseudotyped viruses carrying different emerged spike variants, including Delta and Omicron strains, on HEK293T/hACE2 cells. In cell-free virus infection, GMI dominantly impeded the binding of spike-bearing pseudotyped viruses to ACE2-expressing cells. In cell-to-cell fusion model, GMI could efficiently inhibit spike-mediated syncytium without the requirement of ACE2 downregulation.

CONCLUSIONS

GMI, an FDA-approved dietary ingredient, acts as a multifunctional broad-spectrum antiviral against SARS-CoV-2 and could become a promising candidate for preventing or treating SARS-CoV-2 associated diseases.

RNA-Dependent RNA Polymerase of the Second Human Pegivirus Exhibits a High-Fidelity Feature

The virus-encoded RNA-dependent RNA polymerase (RdRp) is responsible for viral replication, and its fidelity is closely related to viral diversity, pathogenesis, virulence, and fitness. Hepatitis C virus (HCV) and the second human pegivirus (HPgV-2) belong to the family Flaviviridae and share some features, including similar viral genome structure. Unlike HCV, HPgV-2 preserves a highly conserved genome sequence and low intrahost variation. However, the underlying mechanism remains to be elucidated. In this study, we evaluated the fidelity of HPgV-2 and HCV RdRp in an in vitro RNA polymerase reaction system. The results showed higher fidelity of HPgV-2 RdRp than HCV NS5B with respect to the misincorporation rate due to their difference in recognizing nucleoside triphosphate (NTP) substrates. Furthermore, HPgV-2 RdRp showed lower sensitivity than HCV to sofosbuvir, a nucleotide inhibitor against HCV RdRp, which explained the insusceptibility of HPgV-2 to direct-acting antiviral (DAA) therapy against HCV infection. Our results indicate that HPgV-2 could be an excellent model for studying the mechanisms involved in viral polymerase fidelity as well as RNA virus diversity and evolution. IMPORTANCE RNA viruses represent the most important pathogens for humans and animals and exhibit rapid evolution and high adaptive capacity, which is due to the high mutation rates for using the error-prone RNA-dependent RNA polymerase (RdRp) during replication. The fidelity of RdRp is closely associated with viral diversity, fitness, and pathogenesis. Previous studies have shown that the second human pegivirus (HPgV-2) exhibits a highly conserved genome sequence and low intrahost variation, which might be due to the fidelity of HPgV-2 RdRp. In this work, we used a series of in vitro RNA polymerase assays to evaluate the in vitro fidelity of HPgV-2 RdRp and compared it with that of HCV RdRp. The results indicated that HPgV-2 RdRp preserves significantly higher fidelity than HCV RdRp, which might contribute to the conservation of the HPgV-2 genome. The unique feature of HPgV-2 RdRp fidelity provides a new model for investigation of viral RdRp fidelity.

Dehydrative Cross-Coupling for C-N Bond Construction under Transition-Metal-Free Conditions

A transition-metal-free catalytic system was designed to address the dehydrative cross-coupling of unactivated primary/secondary alcohols with amines/amides under environmentally benign conditions. Mg²⁺ and counteranion (PF₆^-) worked synergistically to realize C-OH bond cleavage and concomitant C-N bond formation. A wide range of allylic alcohols and amines/amides were tolerated well in this transformation, which allowed C-N bond construction with high efficiency.

Insights into HIV-1 Reverse Transcriptase (RT) Inhibition and Drug Resistance from Thirty Years of Structural Studies

The enzyme reverse transcriptase (RT) plays a central role in the life cycle of human immunodeficiency virus (HIV), and RT has been an important drug target. Elucidations of the RT structures trapping and detailing the enzyme at various functional and conformational states by X-ray crystallography have been instrumental for understanding RT activities, inhibition, and drug resistance. The structures have contributed to anti-HIV drug development. Currently, two classes of RT inhibitors are in clinical use. These are nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). However, the error-prone viral replication generates variants that frequently develop resistance to the available drugs, thus warranting a continued effort to seek more effective treatment options. RT also provides multiple additional potential druggable sites. Recently, the use of single-particle cryogenic electron microscopy (cryo-EM) enabled obtaining structures of NNRTI-inhibited HIV-1 RT/dsRNA initiation and RT/dsDNA elongation complexes that were unsuccessful by X-ray crystallography. The cryo-EM platform for the structural study of RT has been established to aid drug design. In this article, we review the roles of structural biology in understanding and targeting HIV RT in the past three decades and the recent structural insights of RT, using cryo-EM.

Ultralarge Virtual Screening Identifies SARS-CoV-2 Main Protease Inhibitors with Broad-Spectrum Activity against Coronaviruses

Drugs targeting SARS-CoV-2 could have saved millions of lives during the COVID-19 pandemic, and it is now crucial to develop inhibitors of coronavirus replication in preparation for future outbreaks. We explored two virtual screening strategies to find inhibitors of the SARS-CoV-2 main protease in ultralarge chemical libraries. First, structure-based docking was used to screen a diverse library of 235 million virtual compounds against the active site. One hundred top-ranked compounds were tested in binding and enzymatic assays. Second, a fragment discovered by crystallographic screening was optimized guided by docking of millions of elaborated molecules and experimental testing of 93 compounds. Three inhibitors were identified in the first library screen, and five of the selected fragment elaborations showed inhibitory effects. Crystal structures of target-inhibitor complexes confirmed docking predictions and guided hit-to-lead optimization, resulting in a noncovalent main protease inhibitor with nanomolar affinity, a promising in vitro pharmacokinetic profile, and broad-spectrum antiviral effect in infected cells.

A natural product, (S)-10-Hydroxycamptothecin inhibits pseudorabies virus proliferation through DNA damage dependent antiviral innate immunity

Pseudorabies virus (PRV), a member of the subfamily alphaherpesvirinae, is one of the most important pathogenes that cause acute death in infected pigs and leads to substantial economic losses in the global swine industry. Recently, China's emerging PRV mutant strains resulted in the traditionally commercial vaccines not providing complete protection. Some studies reported that PRV could infect humans and cause endophthalmitis and encephalitis under certain circumstances. It is necessary to develop alternative manners to control the virus infection. Here, by screening a library of natural products, (S)-10-Hydroxycamptothecin (10-HCPT) was revealed to inhibit PRV replication with a selective index of 270.04. And 10-HCPT inhibited PRV replication by blocking the viral genome replication but not inhibiting the viral attachment, internalization, and release. RNA interference assay showed that 10-HCPT inhibited PRV replication by targeting DNA topoisomerase 1 (TOP1). Meanwhile, 10-HCPT treatment induced DNA damage response and stimulated antiviral innate immunity. Animal challenge experiments showed that 10-HCPT effectively alleviated clinical signs and hispathology, and increased INF-β responses in lung and brain tissues of mice induced by PRV infection. The results demonstrate that 10-HCPT is a promising therapeutic agent to control PRV infection.

From cell lines to cancer patients: personalized drug synergy prediction

MOTIVATION

Combination drug therapies are effective treatments for cancer. However, the genetic heterogeneity of the patients and exponentially large space of drug pairings pose significant challenges for finding the right combination for a specific patient. Current in silico prediction methods can be instrumental in reducing the vast number of candidate drug combinations. However, existing powerful methods are trained with cancer cell line gene expression data, which limits their applicability in clinical settings. While synergy measurements on cell line models are available at large scale, patient-derived samples are too few to train a complex model. On the other hand, patient-specific single-drug response data are relatively more available.

RESULTS

In this work, we propose a deep learning framework, Personalized Deep Synergy Predictor (PDSP), that enables us to use the patient-specific single drug response data for customizing patient drug synergy predictions. PDSP is first trained to learn synergy scores of drug pairs and their single drug responses for a given cell line using drug structures and large scale cell line gene expression data. Then, the model is fine-tuned for patients with their patient gene expression data and associated single drug response measured on the patient ex vivo samples. In this study, we evaluate PDSP on data from three leukemia patients and observe that it improves the prediction accuracy by 27% compared to models trained on cancer cell line data.

AVAILABILITY AND IMPLEMENTATION

PDSP is available at https://github.com/hikuru/PDSP.

Identification of gp120 Residue His105 as a Novel Target for HIV-1 Neutralization by Small-Molecule CD4-Mimics

The design and synthesis of butyl chain derivatives at the indane ring 3-position of our lead CD4-mimetic compound BNM-III-170 that inhibits human immunodeficiency virus (HIV-1) infection are reported. Optimization efforts were guided by crystallographic and computational analysis of the small-molecule ligands of the Phe43 cavity of the envelope glycoprotein gp120. Biological evaluation of 11-21 revealed that members of this series of CD4-mimetic compounds are able to inhibit HIV-1 viral entry into target cells more potently and with greater breadth compared to BNM-III-170. Crystallographic analysis of the binding pocket of 14, 16, and 17 revealed a novel hydrogen bonding interaction between His105 and a primary hydroxyl group on the butyl side chain. Further optimization of this interaction with the His105 residue holds the promise of more potent CD4-mimetic compounds.

Prediction of synergistic drug combinations using PCA-initialized deep learning

Background

Cancer is one of the main causes of death worldwide. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. However, the immense number of possible drug combinations and large synergistic space makes it infeasible to screen all effective drug pairs experimentally. Therefore, it is crucial to develop computational approaches to predict drug synergy and guide experimental design for the discovery of rational combinations for therapy.

Results

We present a new deep learning approach to predict synergistic drug combinations by integrating gene expression profiles from cell lines and chemical structure data. Specifically, we use principal component analysis (PCA) to reduce the dimensionality of the chemical descriptor data and gene expression data. We then propagate the low-dimensional data through a neural network to predict drug synergy values. We apply our method to O’Neil’s high-throughput drug combination screening data as well as a dataset from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. We compare the neural network approach with and without dimension reduction. Additionally, we demonstrate the effectiveness of our deep learning approach and compare its performance with three state-of-the-art machine learning methods: Random Forests, XGBoost, and elastic net, with and without PCA-based dimensionality reduction.

Conclusions

Our developed approach outperforms other machine learning methods, and the use of dimension reduction dramatically decreases the computation time without sacrificing accuracy.

GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction

OBJECTIVE

To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.

MATERIALS AND METHODS

We propose a deep learning framework named Graph Convolutional Network for Drug Synergy (GraphSynergy). GraphSynergy adapts a spatial-based Graph Convolutional Network component to encode the high-order topological relationships in the PPI network of protein modules targeted by a pair of drugs, as well as the protein modules associated with a specific cancer cell line. The pharmacological effects of drug combinations are explicitly evaluated by their therapy and toxicity scores. An attention component is also introduced in GraphSynergy, which aims to capture the pivotal proteins that play a part in both PPI network and biomolecular interactions between drug combinations and cancer cell lines.

RESULTS

GraphSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the 2 latest drug combination datasets. Specifically, GraphSynergy achieves accuracy values of 0.7553 (11.94% improvement compared to DeepSynergy, the latest published drug combination prediction algorithm) and 0.7557 (10.95% improvement compared to DeepSynergy) on DrugCombDB and Oncology-Screen datasets, respectively. Furthermore, the proteins allocated with high contribution weights during the training of GraphSynergy are proved to play a role in view of molecular functions and biological processes, such as transcription and transcription regulation.

CONCLUSION

The introduction of topological relations between drug combination and cell line within the PPI network can significantly improve the capability of synergistic drug combination identification.

In silico identification of widely used and well-tolerated drugs as potential SARS-CoV-2 3C-like protease and viral RNA-dependent RNA polymerase inhibitors for direct use in clinical trials

Despite strict measures taken by many countries, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to be an issue of global concern. Currently, there are no clinically proven pharmacotherapies for coronavirus disease 2019, despite promising initial results obtained from drugs such as azithromycin and hydroxychloroquine. Therefore, the repurposing of clinically approved drugs for use against SARS-CoV-2 has become a viable strategy. Here, we searched for drugs that target SARS-CoV-2 3C-like protease (3CLpro) and viral RNA-dependent RNA polymerase (RdRp) by in silico screening of the U.S. Food and Drug Administration approved drug library. Well-tolerated and widely used drugs were selected for molecular dynamics (MD) simulations to evaluate drug-protein interactions and their persistence under physiological conditions. Tetracycline, dihydroergotamine, ergotamine, dutasteride, nelfinavir, and paliperidone formed stable interactions with 3CLpro based on MD simulation results. Similar analysis with RdRp showed that eltrombopag, tipranavir, ergotamine, and conivaptan bound to the enzyme with high binding free energies. Docking results suggest that ergotamine, dihydroergotamine, bromocriptine, dutasteride, conivaptan, paliperidone, and tipranavir can bind to both enzymes with high affinity. As these drugs are well tolerated, cost-effective, and widely used, our study suggests that they could potentially to be used in clinical trials for the treatment of SARS-CoV-2-infected patients.Communicated by Ramaswamy H. Sarma.

The contribution of bovines to human health against viral infections

In the last 40 years, novel viruses have evolved at a much faster pace than other pathogens. Viral diseases pose a significant threat to public health around the world. Bovines have a longstanding history of significant contributions to human nutrition, agricultural, industrial purposes, medical research, drug and vaccine development, and livelihood. The life cycle, genomic structures, viral proteins, and pathophysiology of bovine viruses studied in vitro paved the way for understanding the human counterparts. Calf model has been used for testing vaccines against RSV, papillomavirus vaccines and anti-HCV agents were principally developed after using the BPV and BVDV model, respectively. Some bovine viruses-based vaccines (BPIV-3 and bovine rotaviruses) were successfully developed, clinically tried, and commercially produced. Cows, immunized with HIV envelope glycoprotein, produced effective broadly neutralizing antibodies in their serum and colostrum against HIV. Here, we have summarized a few examples of human viral infections for which the use of bovines has contributed to the acquisition of new knowledge to improve human health against viral infections covering the convergence between some human and bovine viruses and using bovines as disease models. Additionally, the production of vaccines and drugs, bovine-based products were covered, and the precautions in dealing with bovines and bovine-based materials.

Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches

Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand- and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.

Recent Advances Towards Treatment of HIV: Synthesis and SAR Studies

In the present study, authors want to encourage the research exertions through structureactivity relationship for the identification of effective molecules for the treatment of Human immunodeficiency virus because nowadays AIDS is considered as one of the main causes of death in human beings. A diversity of biological resources has been searched and developed for the treatment of HIV but unfortunately, until now, no medicine is found to be fully effective and safe for the cure of patients. Human immunodeficiency virus is a type of lentivirus which causes the infection of HIV and once it enters the human body, it stays for a longer period of time triggering immunodeficiency syndrome. For searching and developing new potent and effective anti-HIV molecules, medicinal chemists have engaged in countless targets with the structure-activity relationship (SAR) of molecules and on this basis, many antiretroviral therapies have been developed to cure HIV infection. Most of these new searched molecules have been found to be clinically active against various types of AIDS patient and auxiliary research in this area may lead to better treatment in the near future. This article encompasses and highlights the recent advancement of innumerable inhibitors laterally through synthetic, semi-synthetic and structure-activity relationship approaches.

Hydrophobic Pocket Occupation Design of Difluoro-Biphenyl-Diarylpyrimidines as Non-Nucleoside HIV-1 Reverse Transcriptase Inhibitors: from N-Alkylation to Methyl Hopping on the Pyrimidine Ring

Considering the nonideal metabolic stability of the difluoro-biphenyl-diarylpyrimidine lead compound 4, a series of novel alkylated difluoro-biphenyl-diarylpyrimidines were designed and synthesized based on their structure. Introducing alkyl or substituted alkyl groups on the linker region to block the potential metabolic sensitive sites generated 22 derivatives. Among them, compound 12a with an N-methyl group displayed excellent anti-HIV-1 activity and selectivity. The methyl group was hopped to the central pyrimidine to occupy the small linker region and maintain the water-mediated hydrogen bond observed in the binding of compound 4 with RT. The resulting compound 16y exhibited an improved anti-HIV-1 activity, much lower cytotoxicity, and nanomolar activity toward multiple mutants. In addition, 16y has a better stability in human liver microsomes than 4. Moreover, no apparent in vivo acute toxicity was observed in 16y-treated female, especially pregnant mice. This series of alkylated compounds with highly potency and safety represent a promising lead template for future discovery.

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.

Recent Progress on Biomaterials Fighting against Viruses

Viruses not only pose severe threats to public health, but also influence the development of society. Over the past decade, rapid advances have been seen in the application of nanomaterials to virus research. As an interdisciplinary field, nanotechnology offers powerful functions because the structures of nanomaterials are unique, with remarkable physicochemical properties and excellent biocompatibility. Nanomaterials have been developed for virus detection and tracking and for antiviral strategies, to better understand viruses and reduce viral infections, implying a bright future for this field. Herein, the recent advances are systematically summarized regarding the nanomaterials used in viral studies. Representative applications of nanomaterials to viral detection and tracking are described. The antiviral effects achieved with nanomaterials based on different mechanisms are also described, including entry inhibition, inhibition of viral replication, and immunological enhancement. The current challenges and future opportunities in this promising field are also discussed.

Advances in Nucleoside and Nucleotide Analogues in Tackling Human Immunodeficiency Virus and Hepatitis Virus Infections

Nucleoside and nucleotide analogues are structurally similar antimetabolites and are promising small-molecule chemotherapeutic agents against various infectious DNA and RNA viruses. To date, these analogues have not been documented in-depth as anti-human immunodeficiency virus (HIV) and anti-hepatitis virus agents, these are at various stages of testing ranging from pre-clinical, to those withdrawn from trials, or those that are approved as drugs. Hence, in this review, the importance of these analogues in tackling HIV and hepatitis virus infections is discussed with a focus on the viral genome and the mechanism of action of these analogues, both in a mutually exclusive manner and their role in HIV/hepatitis coinfection. This review encompasses nucleoside and nucleotide analogues from 1987 onwards, starting with the first nucleoside analogue, zidovudine, and going on to those in current clinical trials and even the drugs that have been withdrawn. This review also sheds light on the prospects of these nucleoside analogues in clinical trials as a treatment option for the COVID-19 pandemic.

Druggability modification strategies of the diarylpyrimidine-type non-nucleoside reverse transcriptase inhibitors

Drug discovery of human immunodeficiency virus (HIV) is a hot field in medicinal chemistry community for many years. The diarylpyrimidines (DAPYs) are the second-generation non-nucleoside reverse transcriptase inhibitors (NNRTIs) targeting reverse transcriptase, playing a great irreplaceable role in HIV transcriptional therapy. However, fast-growing drug-resistant mutations as nonnegligible challenge are still unpredictably appeared in the clinical practice, leading to deactivate or reduce the existing drugs. In the last 20 years, more and more novel DAPY derivatives have developed with the purpose to counter the mutants. Nevertheless, most of them have dissatisfactory pharmacokinetics (PK) or poor antiviral activity toward resistant mutant strains. In this article, we will analyze the NNRTI derivatives with promising druggability, and summarize a series of druggability modification strategies to improve the antiviral activity, reduce toxicity and improve the PK properties in recent years. The prospects of DAPYs and the directions for future efforts will be discussed.

Azafluorene derivatives as inhibitors of SARS CoV-2 RdRp: Synthesis, physicochemical, quantum chemical, modeling and molecular docking analysis

The crystal structures of 2-(1H-indol-3-yl)-4-phenyl-5H-indeno [( Cheng et al., 2007; Lee et al., 2003) 1,21,2-b]pyridine-3-carbonitrile (Ia) and 2-(1H-indol-3-yl)-4-(4-methoxyphenyl)-5H-indeno [( Cheng et al., 2007; Lee et al., 2003) 1,21,2-b]pyridine-3-carbonitrile (Ib) were determined using single crystal X-ray diffraction. Both the compounds belong to the triclinic system with the P-1 space group. The azafluorene ring system in both the compounds is effectively planar. The intermolecular interactions present in the compounds are discussed using Hirshfeld surface analysis, QTAIM and NCI. Compound Ib formed a strong interaction (-24.174 kJ/mol) with the solvent molecule. Both the compounds were geometry optimized using DFT/B3LYP level of theory. The compound's drug-like behaviors were studied using HOMO-LUMO analysis. The homology modeling of SARS CoV-2 RdRp was done utilizing the PDB 6NUR_A as a template. The model showed above 99% similarity with its preceder SARS CoV. The molecular docking analysis of the synthesized compounds was carried out along with some suggested drugs for COVID-19 and some phytochemicals. The docking results were then analyzed. The binding free energy of the complexes were calculated using MM-PB(GB)SA and ADMET properties of Ia and Ib were also predicted. Some suggestions are given from this analysis.

Prediction of potential inhibitors for RNA-dependent RNA polymerase of SARS-CoV-2 using comprehensive drug repurposing and molecular docking approach

The pandemic prevalence of COVID-19 has become a very serious global health issue. Scientists all over the world have been seriously attempting in the discovery of a drug to combat SARS-CoV-2. It has been found that RNA-dependent RNA polymerase (RdRp) plays a crucial role in SARS-CoV-2 replication, and thus could be a potential drug target. Here, comprehensive computational approaches including drug repurposing and molecular docking were employed to predict an effective drug candidate targeting RdRp of SARS-CoV-2. This study revealed that Rifabutin, Rifapentine, Fidaxomicin, 7-methyl-guanosine-5'-triphosphate-5'-guanosine and Ivermectin have a potential inhibitory interaction with RdRp of SARS-CoV-2 and could be effective drugs for COVID-19. In addition, virtual screening of the compounds from ZINC database also allowed the prediction of two compounds (ZINC09128258 and ZINC09883305) with pharmacophore features that interact effectively with RdRp of SARS-CoV-2, indicating their potentiality as effective inhibitors of the enzyme. Furthermore, ADME analysis along with analysis of toxicity was also undertaken to check the pharmacokinetics and drug-likeness properties of the two compounds. Comparative structural analysis of protein-inhibitor complexes revealed that the amino acids Y32, K47, Y122, Y129, H133, N138, D140, T141, S709 and N781 are crucial for drug surface hotspot in the RdRp of SARS-CoV-2.

The nucleocapsid protein of zoonotic betacoronaviruses is an attractive target for antiviral drug discovery

Betacoronaviruses are in one genera of coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome-related coronavirus (MERS-CoV), etc. These viruses threaten public health and cause dramatic economic losses. The nucleocapsid (N) protein is a structural protein of betacoronaviruses with multiple functions such as forming viral capsids with viral RNA, interacting with viral membrane protein to form the virus core with RNA, binding to several cellular kinases for signal transductions, etc. In this review, we highlighted the potential of the N protein as a suitable antiviral target from different perspectives, including structure, functions, and antiviral strategies for combatting betacoronaviruses.

Synthesis and Antiviral Activity of a Series of 2'-C-Methyl-4'-thionucleoside Monophosphate Prodrugs

The NS5B RNA-dependent RNA polymerase of the hepatitis C virus (HCV) is a validated target for nucleoside antiviral drug therapy. We endeavored to synthesize and test a series of 4'-thionucleosides with a monophosphate prodrug moiety for their antiviral activity against HCV and other related viruses in the Flaviviridae family. Nucleoside analogs were prepared via the stereoselective Vorbrüggen glycosylation of various nucleobases with per-acetylated 2-C-methyl-4-thio-d-ribose built in a 10-step synthetic sequence from the corresponding ribonolactone. Conjugation of the thionucleoside to a ProTide phosphoramidate allowed for evaluation of the prodrugs in the cellular HCV replicon assay with anti-HCV activities ranging from single-digit micromolar (μM) to >200 μM. The diminished anti-HCV potency of our best compound compared to its 4'-oxo congener is the subject of ongoing research in our lab and is proposed to stem from changes in sugar geometry imparted by the larger sulfur atom.

The Enzyme-Free Release of Nucleotides from Phosphoramidates Depends Strongly on the Amino Acid

Phosphoramidates composed of an amino acid and a nucleotide analogue are critical metabolites of prodrugs, such as remdesivir. Hydrolysis of the phosphoramidate liberates the nucleotide, which can then be phosphorylated to become the pharmacologically active triphosphate. Enzymatic hydrolysis has been demonstrated, but a spontaneous chemical process may also occur. We measured the rate of enzyme-free hydrolysis for 17 phosphoramidates of ribonucleotides with amino acids or related compounds at pH 7.5. Phosphoramidates of proline hydrolyzed fast, with a half-life time as short as 2.4 h for Pro-AMP in ethylimidazole-containing buffer at 37 °C; 45-fold faster than Ala-AMP and 120-fold faster than Phe-AMP. Crystal structures of Gly-AMP, Pro-AMP, βPro-AMP and Phe-AMP bound to RNase A as crystallization chaperone showed how well the carboxylate is poised to attack the phosphoramidate, helping to explain this reactivity. Our results are significant for the design of new antiviral prodrugs.

Benzothiazoles as potential antiviral agents

OBJECTIVES

The recent viral pandemic poses a unique challenge for healthcare providers. Despite the remarkable progress, the number of novel antiviral agents in the pipeline is woefully inadequate against the evolving virulence and drug resistance of current viruses. This highlights the urgent need for new and improved vaccines, diagnostics and therapeutic agents to obviate the viral pandemic.

KEY FINDINGS

Benzothiazole plays a pivotal role in the design and development of antiviral drugs. This is evident from the fact that it comprises many clinically useful agents. The current review is aimed to provide an insight into the recent development of benzothiazole-based antiviral agents, with a special focus on their structure-activity relationships and lead optimisation. One hundred and five articles were initially identified, and from these studies, 64 potential novel lead molecules and main findings were highlighted in this review.

SUMMARY

We hope this review will provide a logical perspective on the importance of improving the future designs of novel broad-spectrum benzothiazole-based antiviral agents to be used against emerging viral diseases.

Natural RNA dependent RNA polymerase inhibitors: Molecular docking studies of some biologically active alkaloids of Argemone mexicana

COVID-19 has become disastrous for world and spread all over. Researchers all around the globe are working to discover a drug to cure from COVID-19. RNA dependent RNA polymerase plays a key role in SARS-CoV-2 replication and thus it could be a potential target for SARS-CoV-2. This study revealed that Protopine, Allocryptopine and (±) 6- Acetonyldihydrochelerythrine could be potential RdRp inhibitors of SARS-CoV-2.

Single atom changes in newly synthesized HIV protease inhibitors reveal structural basis for extreme affinity, high genetic barrier, and adaptation to the HIV protease plasticity

HIV-1 protease inhibitors (PIs), such as darunavir (DRV), are the key component of antiretroviral therapy. However, HIV-1 often acquires resistance to PIs. Here, seven novel PIs were synthesized, by introducing single atom changes such as an exchange of a sulfur to an oxygen, scission of a single bond in P2′-cyclopropylaminobenzothiazole (or -oxazole), and/or P1-benzene ring with fluorine scan of mono- or bis-fluorine atoms around DRV’s scaffold. X-ray structural analyses of the PIs complexed with wild-type Protease (PR_WT) and highly-multi-PI-resistance-associated PR_DRV^R_P51 revealed that the PIs better adapt to structural plasticity in PR with resistance-associated amino acid substitutions by formation of optimal sulfur bond and adaptation of cyclopropyl ring in the S2′-subsite. Furthermore, these PIs displayed increased cell permeability and extreme anti-HIV-1 potency compared to DRV. Our work provides the basis for developing novel PIs with high potency against PI-resistant HIV-1 variants with a high genetic barrier.