Abstract: Systems, methods, and devices predict pharmacokinetic parameters following administration of a formulation, such as a medium-chain triglyceride (MCT). The method includes generating training data indicating human metabolism outcomes of the formulation. The training data includes stomach emptying parameters, a digestion rate, an absorption constant, or a conversion rate with a training target variable being ketone concentration or plasma free fatty acid concentration. The method also includes training a physiologically based biopharmaceutics (PBB) model with the training data and using the PBB model to predict, using the patient data, at least one of a target plasma free fatty acid concentration or a target ketone concentration. Furthermore, as part of a parameter sensitivity analysis of the PBB model, the method varies input parameters of the training data against a simulated mean plasma free fatty acid profile or a simulated mean ketone plasma profile.

Abstract: This invention relates to high drug load compositions of medium chain triglycerides (MCT), and to methods for treatment with such compositions at amounts effective to elevate ketone body concentrations so as to treat conditions associated with reduced neuronal metabolism, for example Alzheimer's disease.

Abstract: The present invention provides methods and compositions for the delivery of a biologically active agent to a biological system. The compositions include the active agent and a lyotropic phase and release of the active agent to the biological system is modified by the lyotropic phase.

Abstract: The invention provides a compound containing a head group based on urea, glycerol or glycerate and a tail selected from the group consisting of a branched alkyl chain, a branched alkyloxy chain or an alkenyl chain. The compounds may be used as surfactants to form a lyotropic phase that is stable in excess polar solution.