Alan Talevi

1. The pharmaceutical industry has experienced a sustained decline in R&D productivity over the past several decades. Multiple factors have been proposed to explain this trend, including increasing...

Background/Objectives: Dravet Syndrome (DS) is a severe form of epilepsy that typically manifests in the first year of life and often requires polytherapy with two or more antiseizure medications (ASMs) to achieve adequate seizure control. Whereas the combination of stiripentol (STP) and cannabidiol (CBD) has demonstrated clinical efficacy, it presents significant formulation challenges due to the low aqueous solubility and poor oral bioavailability of both compounds. Furthermore, the high daily dosages of STP (approximately 50 mg/kg/day or higher) and the oily nature of conventional CBD formulations often hinder patient compliance, as pediatric patients frequently reject these treatments due to unfavorable organoleptic properties. Methods: Nanostructured lipid carriers (NLCs) containing STP and CBD suspended in an aqueous medium were developed. The formulation was optimized using Response Surface Methodology (RSM) and subjected to comprehensive in vitro and in vivo characterization. Results: The optimized formulation exhibited a mean particle size of 175.3 nm, a polydispersity index (PDI) of 0.232, a zeta potential of −8.35 mV, and an encapsulation efficiency greater than 99% for both drugs. Physicochemical characterization via atomic force microscopy, differential scanning calorimetry, thermogravimetric analysis, X-ray diffraction, and Fourier transform infrared spectroscopy revealed spherical nanoparticles without aggregation, with the drugs molecularly dispersed within the lipid matrix. Both STP and CBD showed sustained release profiles and demonstrated oral pharmacokinetic profiles that were comparable or superior to current commercial products. Conclusions: This novel formulation represents a promising therapeutic alternative for DS, enabling the co-administration of STP and CBD while potentially enhancing CBD bioavailability and treatment adherence in pediatric populations.

Echinococcosis is a Neglected Tropical Disease (NTD) caused by Echinococcus granulosus and Echinococcus multilocularis , the etiological agents of cystic and alveolar echinococcosis, respectively. These infections pose a significant public health burden, particularly in endemic regions. Cestodes lack key enzymes involved in lipid metabolism and must acquire lipids from their hosts. Fatty Acid Binding Proteins (FABPs), which mediate lipid trafficking and intracellular transport, have therefore emerged as essential and potentially druggable targets. In this study, we implemented an integrated virtual screening strategy combining ligand-based and structure-based approaches to identify novel FABP binders as potential therapeutic agents against Echinococcus spp. High-specificity screening of approximately 435,000 compounds yielded a limited number of prioritized in silico hits. Four compounds—hydrochlorothiazide, naratriptan, fenticonazole, and montelukast—were selected for experimental validation, prioritizing repurposing candidates. Fluorescence displacement assays confirmed that hydrochlorothiazide binds to three cestode FABPs (EgFABP1, EmFABP1, and EmFABP3), validating the predictive performance of the computational workflow. These findings support the value of parallel in silico screening strategies and drug repurposing approaches for the discovery of new therapeutic candidates against neglected tropical diseases.

Question 1: Your academic journey spans pharmacy, computational chemistry, and biopharmacy. In your recent article, you discuss the transformative role of AI in expanding confined chemical spaces. ...

Background/Objectives: Chagas disease is a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi, which currently affects around 8 million people worldwide. The therapeutic arsenal against T. cruzi is so far limited to only two approved drugs, benznidazole and nifurtimox, that have considerable side effects and limited efficacy in the chronic stage of the disease. Here, we have resorted to supervised phenotypic machine learning models to explore drug repurposing opportunities and identify potential new therapeutic solutions for Chagas disease. Methods: More than 100,000 bioactivity data points were retrieved from ChEMBL and carefully curated according to the data-centric machine learning paradigm. After curation, two datasets comprising 344 compounds tested against T. cruzi Y strain trypomastigotes and 785 compounds tested against Tulahuen strain amastigotes were obtained and used to infer ensemble learning models with excellent average and early enrichment metrics in retrospective screening experiments (AUROC > 0.96 and EF_0.01 > 58). A prospective screening campaign was then performed on DrugBank and the Drug Repurposing Hub databases, submitting eight in silico hits for experimental confirmation. Results: Six of the in silico hits confirmed their predicted trypanocidal effects. Conclusions: We have built portable meta-classifiers capable of identifying small molecules with trypanocidal activity against amastigotes, the clinically most relevant stage of T. cruzi. The predictive ability of this meta-classifier was experimentally validated.