An integrated chemical biology approach reveals the mechanism of action of HIV replication inhibitors

Novel dihydropyrimidines as promising EGFR & HER2 inhibitors: Insights from experimental and computational studies

Dihydropyrimidines are widely recognized for their diverse biological properties and are often synthesized by the Biginelli reactions. In this backdrop, a novel series of Biginelli dihydropyrimidines were designed, synthesized, purified, and analyzed by FT-IR, 1H NMR, 13C NMR, and mass spectrometry. Anticancer activity against MCF-7 breast cancer cells was evaluated as part of their cytotoxicity in comparison with the normal Vero cells. The cytotoxicity of dihydropyrimidines ranges from moderate to significant. Among the 38 dihydropyrimidines screened, compounds 16, 21, and 39 exhibited significant cytotoxicity. These 3 compounds were subjected to flow cytometry studies and EGFRwt Kinase inhibition assay using lapatinib as a standard. The study included evaluation for the inhibition of EGFR and HER2 expression at five different concentrations. At a concentration of 1000 nM compound 21 showed 98.51 % and 96.79 % inhibition of EGFR and HER2 expression. Moreover, compounds 16, 21 and 39 significantly inhibited EGFRwt activity with IC50 = 69.83, 37.21 and 76.79 nM, respectively. In addition, 3D-QSAR experiments were conducted to elucidate Structure activity relationships in a 3D grid space by comparing the experimental and predicted cytotoxic activities. Molecular docking studies were performed to validate the results by in silico method. All together, we developed a new series of Biginelli dihydropyrimidines as dual EGFR/HER2 inhibitors.

Quinoline-dihydropyrimidin-2(1H)-one hybrids: Synthesis, biological activity and mechanistic studies

A novel class of quinoline-dihydropyrimidin-2(1H)-one (DHPM) hybrids was synthesized and in vitro antiplasmodial activity was evaluated against chloroquine sensitive (D10) and chloroquine resistant (Dd2) strains of Plasmodium falciparum, the human malaria parasite. The antiplasmodial activity was compared to previously reported DHPM based molecular hybrids. Dual mode of antiplasmodial action of the most active member has been evaluated through heme binding study and in silico docking in the active site of dihydrofolate enzymes (wild-type as well as mutant). Favourable pharmacokinetic parameters were predicted in the ADMET evaluation. The new hybrids were also tested against a number of DNA and RNA viruses. No antiviral activity was found, except for one hybrid  that showed mild inhibitory activity against two strains of cytomegalovirus (AD-169 and Davis), The most active hybrid  was found to be a selective inhibitor of the growth of P. falciparum as well as a modest inhibitor of varicella zoster virus in HEL cells. Cytotoxicity of all hybrids was assessed in HEL, HeLa, Vero, MDCK, and CRFK cell cultures.

The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology

Medicinal chemistry plays a fundamental and underlying role in chemical biology, pharmacology, and medicine to discover safe and efficacious drugs. Small molecule medicinal chemistry relies on iterative learning cycles composed of compound design, synthesis, testing, and data analysis to provide new chemical probes and lead compounds for novel and druggable targets. Using traditional approaches, the time from hypothesis to obtaining the results can be protracted, thus limiting the number of compounds that can be advanced into clinical studies. This challenge can be tackled with the recourse of enabling technologies that are showing great potential in improving the drug discovery process. In this Perspective, we highlight recent developments toward innovative medicinal chemistry strategies based on continuous flow systems coupled with automation and bioassays. After a discussion of the aims and concepts, we describe equipment and representative examples of automated flow systems and end-to-end prototypes realized to expedite medicinal chemistry discovery cycles.

Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease

Inadequate patient samples and costly annotated data generations result into the smaller dataset in the biomedical domain. Due to which the predictions with a trained model that usually reveal a single small dataset association are fail to derive robust insights. To cope with the data sparsity, a promising strategy of combining data from the different related tasks is exercised in various application. Motivated by, successful work in the various bioinformatics application, we propose a multitask learning model based on multi-kernel that exploits the dependencies among various related tasks. This work aims to combine the knowledge from experimental studies of the different dataset to build stronger predictive models for HIV-1 protease cleavage sites prediction. In this study, a set of peptide data from one source is referred as 'task' and to integrate interactions from multiple tasks; our method exploits the common features and parameters sharing across the data source. The proposed framework uses feature integration, feature selection, multi-kernel and multifactorial evolutionary algorithm to model multitask learning. The framework considered seven different feature descriptors and four different kernel variants of support vector machines to form the optimal multi-kernel learning model. To validate the effectiveness of the model, the performance parameters such as average accuracy, and area under curve have been evaluated on the suggested model. We also carried out Friedman and post hoc statistical test to substantiate the significant improvement achieved by the proposed framework. The result obtained following the extensive experiment confirms the belief that multitask learning in cleavage site identification can improve the performance.

Exploiting the Nucleophilicity of the Nitrogen Atom of Imidazoles: One-Pot Three-Component Synthesis of Imidazo-Pyrazines

A novel one-pot multicomponent reaction to synthesize substituted imidazopyrazines is described. In brief, 1H-(imidazol-5-yl)-N-substituted methanamines react with aldehydes and isocyanides in methanol at room temperature to give imidazopyrazine derivatives in excellent yields. The imidazole nitrogen atom was able to intercept the nascent nitrilium ion, channeling the reaction toward to the sole formation of imidazopyrazines, suppressing the competitive formation of other possible side products deriving from the reaction with the high-energy nitrilium ion. The number of examples and the variability of the nature of isocyanides, aldehydes, and amine components herein employed, witness the robustness of this novel methodology.

Synthesis, Characterization, and Anticancer Activities Evaluation of Compounds Derived from 3,4-Dihydropyrimidin-2(1H)-one

3,4-dihydropyrimidin-2(1H)-one compounds (DHPMs) possess extensive biological activities and are mainly prepared via Biginelli reaction and N-alkylation. In the present study, selective alkylation of N¹ was investigated by using tetrabutylammonium hydroxide. In vitro cytotoxicity study on all synthesized compounds demonstrated that introduction of the aryl chain in the R³ as well as the low electron-donating group in the R¹ of DHPMs contributed to the anti-proliferative potency. A larger value of the partition coefficient (Log P) and suitable polar surface area (PSA) values were both found to be important in order to maintain the antitumor activity. The results from in vivo study indicated the great potential of compound 3d to serve as a lead compound for novel anti-tumor drugs to treat glioma. Pharmacophore study regarding the structure-activity relations of DHPMs were also conducted. Our results here could provide a guide for the design of novel bioactive 3,4-dihydropyrimidin-2(1H)-one compounds.

Molecular Docking Studies of HIV-1 Resistance to Reverse Transcriptase Inhibitors: Mini-Review

Currently, millions of people are living with human immunodeficiency virus type 1 (HIV-1), which causes acquired immunodeficiency syndrome. However, the spread of the HIV-1 resistance to antiviral agents is the major problem in the antiretroviral therapy and medical management of HIV-infected patients. HIV-1 reverse transcriptase (RT) is one of the key viral targets for HIV-1 inhibition. Therefore, the studies on the combatting the HIV resistance that occurs due to the structural changes in RT, are in great demand. This work aims to provide an overview of the state-of-the-art molecular docking approaches applied to the studies of the HIV-1 resistance, associated with RT structure changes. We have reviewed recent studies using molecular docking with mutant forms of RT. The work discusses the modifications of molecular docking, which have been developed to find the novel molecules active against resistance mutants of RT and/or recombinant strains of HIV-1. The perspectives of the existing algorithms of molecular docking to the studies on molecular mechanisms of resistance and selection of the correct binding poses for the reverse transcriptase inhibitors are discussed.

A Combination of Flow and Batch Mode Processes for the Efficient Preparation of mGlu2/3 Receptor Negative Allosteric Modulators (NAMs)

Benzodiazepinones are privileged scaffolds with activity against multiple therapeutically relevant biological targets. In support of our ongoing studies around allosteric modulators of metabotropic glutamate receptors (mGlus) we required the multigram synthesis of a β-ketoester key intermediate. We report the continuous flow synthesis of tert-butyl 3-(2-cyanopyridin-4-yl)-3-oxopropanoate and its transformation to potent mGlu_2/3 negative allosteric modulators (NAMs) in batch mode.

Automating drug discovery

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.