In vitro activity of nicotinamide/antileishmanial drug combinations

To improve the management of leishmaniasis, new drugs and/or alternative therapeutic strategies are required. Combination therapy of antileishmanial drugs is currently considered as one of the most rational approaches to lower treatment failure rate and limit drug resistance spreading. Nicotinamide (NAm), also known as vitamin B3 that is already is used in human therapy, exerts in vitro antileishmanial activity. Drug combination studies, performed on L. infantum axenic amastigotes, revealed that NAm significantly improves the antileishmanial activity of trivalent antimony in a synergistic manner while it shows additive activity with amphotericin B and slightly antagonizes pentamidine activity. NAm also significantly increases the toxicity of pentavalent antimony against the intracellular forms of L. infantum, L. amazonensis and L. braziliensis. The potential of NAm to be used as adjuvant during leishmaniasis chemotherapy is further discussed.     

Systems pharmacology and enhanced pharmacodynamic models for understanding antibody-based drug action and toxicity

Pharmacokinetic (PK) and pharmacodynamic (PD) models seek to describe the temporal pattern of drug exposures and their associated pharmacological effects produced at micro- and macro-scales of organization. Antibody-based drugs have been developed for a large variety of diseases, with effects exhibited through a comprehensive range of mechanisms of action. Mechanism-based PK/PD and systems pharmacology models can play a major role in elucidating and integrating complex antibody pharmacological properties, such as nonlinear disposition and dynamical intracellular signaling pathways triggered by ligation to their cognate targets. Such complexities can be addressed through the use of robust computational modeling techniques that have proven powerful tools for pragmatic characterization of experimental data and for theoretical exploration of antibody efficacy and adverse effects. The primary objectives of such multi-scale mathematical models are to generate and test competing hypotheses and to predict clinical outcomes. In this review, relevant systems pharmacology and enhanced PD (ePD) models that are used as predictive tools for antibody-based drug action are reported. Their common conceptual features are highlighted, along with approaches used for modeling preclinical and clinically available data. Key examples illustrate how systems pharmacology and ePD models codify the interplay among complex biology, drug concentrations, and pharmacological effects. New hybrid modeling concepts that bridge cutting-edge systems pharmacology models with established PK/ePD models will be needed to anticipate antibody effects on disease in subpopulations and individual patients.