Quantitative systems pharmacology Model to characterize valproic acid-induced hyperammonemia and the effect of L-carnitine supplementation.

Valproic acid (VPA) is a short-chain fatty acid widely prescribed in the treatment of seizure disorders and epilepsy syndromes, although its therapeutic value may be undermined by its toxicity. VPA serious adverse effects are reported to have a significant and dose-dependent incidence, many associated with VPA-induced hyperammonemia. This effect has been linked with reduced levels of carnitine; an endogenous compound involved in fatty acid's mitochondrial ß-oxidation by facilitation of its entrance via the carnitine shuttle. High exposure to VPA can lead to carnitine depletion causing a misbalance between the intra-mitochondrial ß-oxidation and the microsomal ω-oxidation, a pathway that produces toxic metabolites such as 4-en-VPA which inhibits ammonia elimination. Moreover, a reduction in carnitine levels might be also related to VPA-induced obesity and lipids disorder. In turn, L-carnitine supplementation (CS) has been recommended and empirically used to reduce VPA's hepatotoxicity. The aim of this work was to develop a Quantitative Systems Pharmacology (QSP) model to characterize VPA-induced hyperammonemia and evaluate the benefits of CS in preventing hyperammonemia under both chronic treatment and after VPA overdosing. The QSP model included a VPA population pharmacokinetics model that allowed the prediction of total and unbound concentrations after single and multiple oral doses considering its saturable binding to plasma proteins. Predictions of time courses for 2-en-VPA, 4-en-DPA, VPA-glucuronide, carnitine, ammonia and urea levels, and for the relative change in fatty acids, Acetyl-CoA, and glutamate reflected the VPA induced changes and the efficacy of the treatment with L-carnitine. The QSP model was implemented to give a rational basis for the L-carnitine dose selection to optimize CS depending on VPA dosage regime and to assess the currently recommended L-carnitine rescue therapy after VPA overdosing. Results show that a L-carnitine dose equal to the double of the VPA dose using the same interdose interval would maintain the ammonia levels at baseline. The QSP model may be expanded in the future to describe other adverse events linked to VPA-induced changes in endogenous compounds.

Theophylline: Old Drug in a New Light, Application in COVID-19 through Computational Studies.

Theophylline (3-methyxanthine) is a historically prominent drug used to treat respiratory diseases, alone or in combination with other drugs. The rapid onset of the COVID-19 pandemic urged the development of effective pharmacological treatments to directly attack the development of new variants of the SARS-CoV-2 virus and possess a therapeutical battery of compounds that could improve the current management of the disease worldwide. In this context, theophylline, through bronchodilatory, immunomodulatory, and potentially antiviral mechanisms, is an interesting proposal as an adjuvant in the treatment of COVID-19 patients. Nevertheless, it is essential to understand how this compound could behave against such a disease, not only at a pharmacodynamic but also at a pharmacokinetic level. In this sense, the quickest approach in drug discovery is through different computational methods, either from network pharmacology or from quantitative systems pharmacology approaches. In the present review, we explore the possibility of using theophylline in the treatment of COVID-19 patients since it seems to be a relevant candidate by aiming at several immunological targets involved in the pathophysiology of the disease. Theophylline down-regulates the inflammatory processes activated by SARS-CoV-2 through various mechanisms, and herein, they are discussed by reviewing computational simulation studies and their different applications and effects.