Natural products have an important role as prototypes in the synthesis of new anticancer drugs. Piperine is an alkaloid amide with antitumor activity and significant toxicity. Then, the N-(p-nitrophenyl)acetamide piperinoate (HE-02) was synthesized, and tested for toxicological and antitumor effects. The toxicity was evaluated in vitro (on RAW 264.7 cells and mice erythrocytes) and in vivo (acute toxicity in mice). The Ehrlich ascites carcinoma model was used to evaluate the antitumor activity of HE-02 (6.25, 12.5 or 25 mg/kg, intraperitoneally, i.p.), as well as toxicity. HE-02 induced only 5.01% of hemolysis, and reduced the viability of RAW 264.7 cells by 49.75% at 1000 µg/mL. LD50 (lethal dose 50%) was estimated at around 2000 mg/kg (i.p.). HE-02 reduced Ehrlich tumor cell viability and peritumoral microvessels density. There was an increase of Th1 helper T lymphocytes cytokine profile levels (IL-1β, TNF-α, IL-12) and a decrease of Th2 cytokine profile (IL-4, IL-10). Moreover, an increase was observed on reactive oxygen species and nitric oxide production. Weak in vivo toxicological effects were recorded. Our data provide evidence that the piperine analogue HE-02 present low toxicity, and its antitumor effect involves modulation of immune system to a cytotoxic Th1 profile.

Background: Leishmaniasis is a neglected disease that does not have adequate treatment. It affects around 12 million people around the world and is classified as a neglected disease by the World Health Organization. In this context, strategies to obtain new, more active and less toxic drugs should be stimulated. Sources of natural products combined with synthetic and chemoinformatic methodologies are strategies used to obtain molecules that are most likely to be effective against a specific disease. Computer-Aided Drug Design has become an indispensable tool in the pharmaceutical industry and academia in recent years and has been employed during various stages of the drug design process. Objectives: Perform structure- and ligand-based approaches, synthesize and characterize some compounds with materials available in our laboratories to verify the method’s efficiency. Methods: We created a database with 33 cyclic imides and evaluated their potential anti- Leishmanial activity (L. amazonensis and L. donovani) through ligand- and structure-based virtual screening. A diverse set selected from ChEMBL databanks of 818 structures (L. donovani) and 722 structures (L. amazonensis), with tested anti-Leishmanial activity against promastigotes forms, were classified according to pIC50 values to generate and validate a Random Forest model that shows higher statistical indices values. The structures of four different L. donovani enzymes were downloaded from the Protein Data Bank and the imides’ structures were submitted to molecular docking. So, with available materials and technical feasibility of our laboratories, we have synthesized and characterized seven compounds through cyclization reactions between isosafrole and maleic anhydride followed by treatment with different amines to obtain new cyclic imides to evaluate their anti-Leishmanial activity. Results: In silico study allowed us to suggest that the cyclic imides 516, 25, 31, 24, 32, 2, 3, 22 can be tested as potential multitarget molecules for leishmanial treatment, presenting activity probability against four strategic enzymes (Topoisomerase I, N-myristoyltransferase, cyclophilin and Oacetylserine sulfhydrylase). The compounds synthesized and tested presented pIC50 values less than 4.7 for Leishmania amazonensis. Conclusion: After combined approach evaluation, we have synthesized and characterized seven cyclic imides by IR, 1H NMR, 13C-APT NMR, COSY, HETCOR and HMBC. The compounds tested against promastigote forms of L. amazonensis presented pIC50 values less than 4.7, showing that our method was efficient in predicting true negative molecules.

Leishmaniasis is a neglected disease that does not have adequate treatment. To try to solve this problem, we have tested a database with 33 cyclic imides and evaluated their potential antiLeishmanial activity (L. donovani) through ligand-based and structure based virtual screening. A diverse set selected from CHEMBL databanks of 818 structures (L. donovani) with tested antileishmanial activity against promastigotes forms, were classified according pIC50 values in order to generate and validate Random Forest model that show higher statistical indices values. The structure of four different L. donovani enzymes were downloaded from PDB databank and imides structures were submitted to molecular docking. In silico study allowed us to suggest that the cyclic imide 527 can be tested as a potential multitarget molecule for leishmanial treatment, presenting activity against four strategic enzymes to treatment with probability of activity of 60%.

Introduction:Piperine is a natural alkaloid found in Piper nigrum. This work aims to perform a virtual screening of 20 synthetic piperine derivatives with potential antileishmanial activity. Methodology: A classificatory prediction model concerning the Leishmania infantum Promastigote cell form in Knime software was built. The compounds were subjected to Molecular Docking in the Molegro Virtual Docker v.6.0.1 software, using the CYP51 target complexed to the inhibitor Fluconazole obtained from the Protein Data Bank. The molecules under study were submitted to a consensus calculation involving the activity probability obtained in the elaborated model, as well as in the molecular docking simulation performed. Results: The prediction model was more than 78% accurate in the test and in the cross-validation, selecting the 20 synthetic derivatives under study as potentially active for the promastigote form. In Docking, only 9 of the molecules of the 20 molecules selected by the model had better energy than the ligand. In the consensus calculation, nine compounds had a probability above 50%. Among the structures, molecule 20 was considered the one with the best performance in the study developed.Conclusions: The virtual screening performed was able to identify the compounds with the highest probability of activity.