Twenty-six years of HIV science: an overview of anti-HIV drugs metabolism

From the identification of HIV as the agent causing AIDS, to the development of effective antiretroviral drugs, the scientific achievements in HIV research over the past twenty-six years have been formidable. Currently, there are twenty-five anti-HIV compounds which have been formally approved for clinical use in the treatment of AIDS. These compounds fall into six categories: nucleoside reverse transcriptase inhibitors (NRTIs), nucleotide reverse transcriptase inhibitors (NtRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), cell entry inhibitors or fusion inhibitors (FIs), co-receptor inhibitors (CRIs), and integrase inhibitors (INIs). Metabolism by the host organism is one of the most important determinants of the pharmacokinetic profile of a drug. Formation of active or toxic metabolites will also have an impact on the pharmacological and toxicological outcomes. Therefore, it is widely recognized that metabolism studies of a new chemical entity need to be addressed early in the drug discovery process. This paper describes an overview of the metabolism of currently available anti-HIV drugs.Da identificação do HIV como o agente causador da AIDS, ao desenvolvimento de fármacos antirretrovirais eficazes, os avanços científicos na pesquisa sobre o HIV nos últimos vinte e seis anos foram marcantes. Atualmente, existem vinte e cinco fármacos anti-HIV formalmente aprovados pelo FDA para utilização clínica no tratamento da AIDS. Estes compostos são divididos em seis classes: inibidores nucleosídeos de transcriptase reversa (INTR), inibidores nucleotídeos de transcriptase reversa (INtTR), inibidores não-nucleosídeos de transcriptase reversa (INNTR), inibidores de protease (IP), inibidores da entrada celular ou inibidores de fusão (IF), inibidores de co-receptores (ICR) e inibidores de integrase (INI). O metabolismo consiste em um dos maiores determinantes do perfil farmacocinético de um fármaco. A formação de metabólitos ativos ou tóxicos terá impacto nas respostas farmacológicas ou toxicológicas do fármaco. Portanto, é amplamente reconhecido que estudos do metabolismo de uma nova entidade química devem ser realizados durante as fases iniciais do processo de desenvolvimento de fármacos. Este artigo descreve uma abordagem do metabolismo dos fármacos anti-HIV atualmente disponíveis na terapêutica

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds

tackling malaria

Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.publishersversionpublishe

Computational drug discovery for the Zika virus

Few Zika virus (ZIKV) outbreaks had been reported since its first detection in 1947, until the recent epidemics occurred in South America (2014/2015) and expeditiously became a global public health emergency. This arbovirus reached 0.5-1.3 million cases of ZIKV infection in Brazil in 2015 and rapidly spread in new geographic areas such as the Americas. Despite the mild symptoms of the Zika fever, the major concern is related to the related severe neurological disorders, especially microcephaly in newborns. Advances in ZIKV drug discovery have been made recently and constitute promising approaches to ZIKV treatment. In this review, we summarize current computational drug discovery efforts and their applicability to discovery of anti-ZIKV drugs. Lastly, we present successful examples of the use of computational approaches to ZIKV drug discovery

Computationally-guided drug repurposing enables the discovery of kinase targets and inhibitors as new schistosomicidal agents.

The development of novel therapeutics is urgently required for diseases where existing treatments are failing due to the emergence of resistance. This is particularly pertinent for parasitic infections of the tropics and sub-tropics, referred to collectively as neglected tropical diseases, where the commercial incentives to develop new drugs are weak. One such disease is schistosomiasis, a highly prevalent acute and chronic condition caused by a parasitic helminth infection, with three species of the genus Schistosoma infecting humans. Currently, a single 40-year old drug, praziquantel, is available to treat all infective species, but its use in mass drug administration is leading to signs of drug-resistance emerging. To meet the challenge of developing new therapeutics against this disease, we developed an innovative computational drug repurposing pipeline supported by phenotypic screening. The approach highlighted several protein kinases as interesting new biological targets for schistosomiasis as they play an essential role in many parasite's biological processes. Focusing on this target class, we also report the first elucidation of the kinome of Schistosoma japonicum, as well as updated kinomes of S. mansoni and S. haematobium. In comparison with the human kinome, we explored these kinomes to identify potential targets of existing inhibitors which are unique to Schistosoma species, allowing us to identify novel targets and suggest approved drugs that might inhibit them. These include previously suggested schistosomicidal agents such as bosutinib, dasatinib, and imatinib as well as new inhibitors such as vandetanib, saracatinib, tideglusib, alvocidib, dinaciclib, and 22 newly identified targets such as CHK1, CDC2, WEE, PAKA, MEK1. Additionally, the primary and secondary targets in Schistosoma of those approved drugs are also suggested, allowing for the development of novel therapeutics against this important yet neglected disease

Integrative multi-kinase approach for the identification of potent antiplasmodial hits

Malaria is a tropical infectious disease that affects over 219 million people worldwide. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new antimalarial drugs is a global health priority. Multi-target drug discovery is a promising and innovative strategy for drug discovery and it is currently regarded as one of the best strategies to face drug resistance. Aiming to identify new multi-target antimalarial drug candidates, we developed an integrative computational approach to select multi-kinase inhibitors for Plasmodium falciparum calcium-dependent protein kinases 1 and 4 (CDPK1 and CDPK4) and protein kinase 6 (PK6). For this purpose, we developed and validated shape-based and machine learning models to prioritize compounds for experimental evaluation. Then, we applied the best models for virtual screening of a large commercial database of drug-like molecules. Ten computational hits were experimentally evaluated against asexual blood stages of both sensitive and multi-drug resistant P. falciparum strains. Among them, LabMol-171, LabMol-172, and LabMol-181 showed potent antiplasmodial activity at nanomolar concentrations (EC50 15 folds. In addition, LabMol-171 and LabMol-181 showed good in vitro inhibition of P. berghei ookinete formation and therefore represent promising transmission-blocking scaffolds. Finally, docking studies with protein kinases CDPK1, CDPK4, and PK6 showed structural insights for further hit-to-lead optimization studies.7CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP405996/2016-0; 400760/2014-2Sem informação2018/05926-2; 2017/02353-9; 2012/16525-2; 2017/18611-7; 2018/07007-4; 2013/13119-6; 2018/24878-9; 2015/20774-

Evaluation of cytotoxic effect of the combination of a pyridinyl carboxamide derivative and oxaliplatin on NCI-H1299 human non-small cell lung carcinoma cells.

Even with all improvements in both diagnostic and therapeutic techniques, lung cancer remains as the most lethal and prevalent cancer in the world. Therefore, new therapeutic drugs and new strategies of drug combination are necessary to provide treatments that are more efficient. Currently, standard therapy regimen for lung cancer includes platinum drugs, such as cisplatin, oxaliplatin, and carboplatin. Besides of the better toxicity profile of oxaliplatin when compared with cisplatin, peripheral neuropathy remains as a limitation of oxaliplatin dose. This study presents LabMol-12, a new pyridinyl carboxamide derivative with antileishmanial and antichagasic activity, as a new hit for lung cancer treatment, which induces apoptosis dependent of caspases in NCI-H1299 lung cancer cells both in monolayer and 3D culture. Moreover, LabMol-12 allows a reduction of oxaliplatin dose when they are combined, thereby, it is a relevant strategy for reducing the side effects of oxaliplatin with the same response. Molecular modeling studies corroborated the biological findings and suggested that the combined therapy can provide a better therapeutically profile effects against NSCLC. All these findings support the fact that the combination of oxaliplatin and LabMol-12 is a promising drug combination for lung cancer

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach