Towards natural mimetics of metformin and rapamycin.

Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan. Metformin and rapamycin are two FDA-approved mTOR inhibitors proposed for this purpose, exhibiting significant anti-cancer and anti-aging properties beyond their current clinical applications. However, each faces issues with approval for off-label, prophylactic use due to adverse effects. Here, we initiate an effort to identify nutraceuticals-safer, naturally-occurring compounds-that mimic the anti-aging effects of metformin and rapamycin without adverse effects. We applied several bioinformatic approaches and deep learning methods to the Library of Integrated Network-based Cellular Signatures (LINCS) dataset to map the gene- and pathway-level signatures of metformin and rapamycin and screen for matches among over 800 natural compounds. We then predicted the safety of each compound with an ensemble of deep neural network classifiers. The analysis revealed many novel candidate metformin and rapamycin mimetics, including allantoin and ginsenoside (metformin), epigallocatechin gallate and isoliquiritigenin (rapamycin), and withaferin A (both). Four relatively unexplored compounds also scored well with rapamycin. This work revealed promising candidates for future experimental validation while demonstrating the applications of powerful screening methods for this and similar endeavors.

Biowaiver approach for biopharmaceutics classification system class 3 compound metformin hydrochloride using in silico modeling.

The dependency of metformin in vivo disposition on the rate and extent of dissolution was studied. The analysis includes the use of fundamental principles of drug input, permeability, and intestinal transit time within the framework of a compartmental absorption transit model to predict key pharmacokinetic (PK) parameters and then compare the results to clinical data. The simulations show that the maximum plasma concentration (C(max) ) and area under the curve (AUC) are not significantly affected when 100% of drug is released within 2 h of oral dosing, which was confirmed with corresponding human PK data. Furthermore, in vitro dissolution profiles measured in aqueous buffers at pH values of 1.2, 4.5, and 6.8 were slower than in vivo release profiles generated by deconvolution of metformin products that were bioequivalent. On the basis of this work, formulations of metformin that release 100% in vitro in a time period equal to or less than two hours are indicated to be bioequivalent. The use of modeling offers a mechanistic-based approach for demonstrating acceptable bioperformance for metformin formulations without having to resort to in vivo bioequivalence studies and may be more robust than statistical comparison of in vitro release profiles. This work further provides a strategy for considering Biopharmaceutics Classification System (BCS) Class 3 compounds to be included under biowaiver guidelines as for BCS Class 1 compounds.