Analysis of drug-induced effect patterns to link structure and side effects of medicines

The high failure rate of experimental medicines in clinical trials accentuates inefficiencies of current drug discovery processes caused by a lack of tools for translating the information exchange between protein and organ system networks. Recently, we reported that biological activity spectra (biospectra), derived from in vitro protein binding assays, provide a mechanism for assessing a molecule’s capacity to modulate the function of protein-network components. Herein we describe the translation of adverse effect data derived from 1,045 prescription drug labels into effect spectra and show their utility for diagnosing drug-induced effects of medicines. In addition, notwithstanding the limitation imposed by the quality of drug label information, we show that biospectrum analysis, in concert with effect spectrum analysis, provides an alignment between preclinical and clinical drug-induced effects. The identification of this alignment provides a mechanism for forecasting clinical effect profiles of medicines. One of the key functions of preclinical drug discovery is the finetuning of experimental medicines for modulating the information flow in cellular protein networks and relating these changes to disease intervention. The high failure rate of drug candidates in clinical trials, however, accentuates inefficiencies of current processes and implicates as main cause the incomplete translation of drug-induced effects on proteins into medically useful effects on organisms 1 . The misalignment between preclinical and clinical drug-induced effects is due, in part, to the remarkable ability of organisms to compensate for the loss or decline in function of specific proteins by rerouting the information flow in protein networks 2 . At the organism level, protein network perturbations, caused by inhibition or stimulation of the function of individual network components, become visible as a pattern of physical symptoms; it does not matter whether protein network perturbations are caused by a disease or by the administration of a medicine 3‐6 . In spite of these complexities, the high costs associated with failure of experimental medicines in clinical trials underscore the need to improve methods for translating drug-induced effects on proteins into drug-induced effects on whole organisms 7,8 . Achieving this goal is a formidable challenge because preclinical methods for structure-function analysis focus on determination of structure-effect relationships of single protein network components and not on information exchange between protein and organ systems networks. In addition, no precise methods exist for comparing drug-induced effect information of medicines obtained in clinical trails. Hence, the induction of drug effects in clinical trials is highly variable and depends on age, sex, physical condition, genetic variance in drug targets, regulation of disease pathways, differences in metabolizing enzymes, dosage forms, and routes of drug administration. In fact, different dosages of drugs