Selective Bias Virtual Screening for Discovery of Promising Antimalarial Candidates targeting Plasmodium N-Myristoyltransferase.

Malaria remains a significant public health challenge, with Plasmodium vivax being the species responsible for the most prevalent form of the disease. Given the limited therapeutic options available, the search for new antimalarials against P. vivax is urgent. This study aims to identify new inhibitors for P. vivax N-myristoyltransferase (PvNMT), an essential drug target against malaria. Through a validated virtual screening campaign, we prioritized 23 candidates for further testing. In the yeast NMT system, seven compounds exhibit a potential inhibitor phenotype. In vitro antimalarial phenotypic assays confirmed the activity of four candidates while demonstrating an absence of cytotoxicity. Enzymatic assays reveal LabMol-394 as the most promising inhibitor, displaying selectivity against the parasite and a strong correlation within the yeast system. Furthermore, molecular dynamics simulations shed some light into its binding mode. This study constitutes a substantial contribution to the exploration of a selective quinoline scaffold and provides valuable insights into the development of new antimalarial candidates.

Innovative Multistage ML-QSAR Models for Malaria: From Data to Discovery.

Malaria presents a significant challenge to global public health, with around 247 million cases estimated to occur annually worldwide. The growing resistance of Plasmodium parasites to existing therapies underscores the urgent need for new and innovative antimalarial drugs. This study leveraged artificial intelligence (AI) to tackle this complex challenge. We developed multistage Machine Learning Quantitative Structure-Activity Relationship (ML-QSAR) models to effectively analyze large datasets and predict the efficacy of chemical compounds against multiple life cycle stages of Plasmodium parasites. We then selected 16 compounds for experimental evaluation, six of which showed at least dual-stage inhibitory activity and one inhibited all life cycle stages tested. Moreover, explainable AI (XAI) analysis provided insights into critical molecular features influencing model predictions, thereby enhancing our understanding of compound interactions. This study not only empowers the development of advanced predictive AI models but also accelerates the identification and optimization of potential antiplasmodial compounds.

In vitro activities of glycoalkaloids from the Solanum lycocarpum against Leishmania infantum

ABSTRACT Leishmania infantum is an etiologic agent of visceral leishmaniasis. This disease is a neglected disease that can be fatal if not treated and additionally, the few therapeutic option present several drawbacks, including difficult route of administration and toxicity, which turn the search for new therapeutic alternatives necessary. Herein, we evaluated the leishmanicidal in vitro activity of the solanum extract from Solanum lycocarpum A. St.-Hil., Solanaceae, and the isolated alkaloids solasodine, solamargine and solasonine against promastigotes and intracellular amastigotes of L. infantum. Solasodine (IC50-pro = 4.7 µg/ml; IC50-ama = 10.8 µg/ml) and solamargine (IC50-pro = 8.1 µg/ml; IC50-ama = 3.0 µg/ml) exhibited interesting leishmanicidal ativity. Solasonine was approximately four-times (Selective Index 3.7) more selective to the parasite than to the host cells. This data suggest that solasonine might be considered as a potential drug candidate for leishmaniasis treatment.

Risk assessment for breast cancer and BRCA mutations in women with personal and familial history / Avaliação de risco para câncer de mama e mutações dos genes BRCA em mulheres com histórico pessoal e familiar

Risk estimation tools can be used in clinical practice to promote the counseling, prevention, or increase the surveillance against breast cancer development. The present study aimed to estimate the risk for breast cancer and the odds for BRCA1/2 mutations, and to correlate the values found by the different models. Breast cancer risk was determined by the models of Gail, Claus, BRCAPRO and Boadicea; and for the mutations, Myriad II, Penn II BRCAPRO, and Boadicea models were utilized, in women who have or had the disease (n = 16) and their respective first degree female relatives unaffected (n = 25) . Considering non affected women 16% were categorized as high risk for breast cancer development in five years by the Gail model, and all values presented significant correlation among the models (p < 0.05). Among the participants, 12% (5/41) were considered high risk for BRCA mutations. All the models presented significant correlation between the odds of BRCA1/2 mutation risk, except between Myriad II and Boadicea models. Since there is no model that includes all the variables influencing the development of this disease, it is essential to estimate the risk by more than one model before initiating any clinical intervention.

Ferramentas de estimativa de risco podem ser utilizadas na prática clínica para promover o aconselhamento, prevenção, ou aumentar a vigilância contra o desenvolvimento do câncer de mama. O presente estudo teve como objetivo estimar o risco de câncer de mama e a probabilidade de risco para mutação BRCA1/2, e correlacionar os valores encontrados pelos diferentes modelos. O risco de desenvolvimento do Câncer de mama foi estimado pelos modelos de Gail, Claus, BRCAPRO e Boadicea, e para as mutações, os modelos Boadicea Myriad II, Penn II BRCAPRO foram utilizados, em mulheres que têm ou tiveram a doença (n = 16) e seu respectivos parentes de primeiro grau do sexo feminino afetados (n = 25). Considerando-se as mulheres não afetadas 16% foram categorizadas como de alto risco para o desenvolvimento de câncer de mama em cinco anos pelo modelo de Gail, e todos os valores apresentaram correlação significativa entre os modelos (p < 0,05). Entre as participantes, 12% (5/41) foram consideradas de alto risco para mutações dos genes BRCA. Todos os modelos apresentaram correlação significativa entre as probabilidades de risco para a mutação BRCA1/2, exceto entre os modelos Boadicea Myriad e II. Uma vez que não existe um modelo que inclui todas as variáveis que influenciam o desenvolvimento da doença, é essencial estimar o risco por mais de um modelo antes do início de qualquer intervenção clínica.