Plasma Concentration Is Not Enough for Phytochemical Lead Selection When Tissue Distribution, Unbound Exposure, and Site-of-Action Pharmacokinetics Determine Efficacy

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Plasma pharmacokinetics remains the dominant exposure framework in early lead selection, yet plasma concentration may poorly represent the pharmacologically relevant exposure of a phytochemical at its site of action. This perspective examines why total or even unbound plasma exposure can become insufficient when tissue binding, regional partitioning, intracellular access, transporter activity, sequestration, and local concentration–time behavior alter the fraction of compound that reaches the relevant biophase. The analysis distinguishes systemic exposure from site exposure, total from unbound concentration, bulk-tissue accumulation from intracellularly available drug, and measured distribution from mechanistically inferred target access. It further argues that lead ranking should not treat plasma area under the curve or maximum concentration as stand-alone surrogates for efficacy when the intended target resides behind biological barriers or within compartments subject to active transport or binding disequilibrium. The principal contribution is a proposed site-of-action pharmacokinetic perspective for phytochemical lead selection in which plasma data remain necessary but are interpreted alongside evidence for unbound tissue exposure, compartment-specific partitioning, temporal concordance, and measurement or model confidence. This perspective does not assume that tissue measurements always outperform plasma metrics, nor that a single distribution parameter predicts efficacy. Its utility is therefore conditional on target localization, compound chemistry, formulation, biological context, analytical feasibility, and validation of any model used to infer inaccessible compartments. The framework is intended to guide evidence prioritization rather than replace compound-specific pharmacokinetic–pharmacodynamic experiments, and its proposed decision logic requires prospective testing.