Towards Personalized Medicine: Computational Approaches to Support Drug Design and Clinical Decision Making

The future looks bright for a clinical practice that tailors the therapy with the best efficacy and highest safety to a patient. Substantial amounts of funding have resulted in technological advances regarding patient-centered data acquisition --- particularly genetic data. Yet, the challenge of translating this data into clinical practice remains open. To support drug target characterization, we developed a global maximum entropy-based method that predicts protein-protein complexes including the three-dimensional structure of their interface from sequence data. To further speed up the drug development process, we present methods to reposition drugs with established safety profiles to new indications leveraging paths in cellular interaction networks. We validated both methods on known data, demonstrating their ability to recapitulate known protein complexes and drug-indication pairs, respectively. After studying the extent and characteristics of genetic variation with a predicted impact on protein function across 60,607 individuals, we showed that most patients carry variants in drug-related genes. However, for the majority of variants, their impact on drug efficacy remains unknown. To inform personalized treatment decisions, it is thus crucial to first collate knowledge from open data sources about known variant effects and to then close the knowledge gaps for variants whose effect on drug binding is still not characterized. Here, we built an automated annotation pipeline for patient-specific variants whose value we illustrate for a set of patients with hepatocellular carcinoma. We further developed a molecular modeling protocol to predict changes in binding affinity in proteins with genetic variants which we evaluated for several clinically relevant protein kinases. Overall, we expect that each presented method has the potential to advance personalized medicine by closing knowledge gaps about protein interactions and genetic variation in drug-related genes. To reach clinical applicability, challenges with data availability need to be overcome and prediction performance should be validated experimentally.Therapien mit der besten Wirksamkeit und höchsten Sicherheit werden in Zukunft auf den Patienten zugeschnitten werden. Hier haben erhebliche finanzielle Mittel zu technologischen Fortschritten bei der patientenzentrierten Datenerfassung geführt, aber diese Daten in die klinische Praxis zu übertragen, bleibt aktuell noch eine Herausforderung. Um die Wirkstoffforschung in der Charakterisierung therapeutischer Zielproteine zu unterstützen, haben wir eine Maximum-Entropie-Methode entwickelt, die Protein-Interaktionen und ihre dreidimensionalen Struktur aus Sequenzdaten vorhersagt. Darüber hinaus, stellen wir Methoden zur Repositionierung von etablierten Arzneimitteln auf neue Indikationen vor, die Pfade in zellulären Interaktionsnetze nutzen. Diese Methoden haben wir anhand bekannter Daten validiert und ihre Fähigkeit demonstriert, bekannte Proteinkomplexe bzw. Wirkstoff-Indikations-Paare zu rekapitulieren. Unsere Analyse genetischer Variation mit einem Einfluss auf die Proteinfunktion in 60,607 Individuen konnte zeigen, dass nahezu jeder Patient funktionsverändernde Varianten in Medikamenten-assoziierten Genen trägt. Der direkte Einfluss der meisten beobachteten Varianten auf die Medikamenten-Wirksamkeit ist jedoch noch unbekannt. Um dennoch personalisierte Behandlungsentscheidungen treffen zu können, präsentieren wir eine Annotationspipeline für genetische Varianten, deren Wert wir für Patienten mit hepatozellulärem Karzinom illustrieren konnten. Darüber hinaus haben wir ein molekulares Modellierungsprotokoll entwickelt, um die Veränderungen in der Bindungsaffinität von Proteinen mit genetischen Varianten voraussagen. Insgesamt sind wir davon überzeugt, dass jede der vorgestellten Methoden das Potential hat, Wissenslücken über Proteininteraktionen und genetische Variationen in medikamentenbezogenen Genen zu schlie{\ss}en und somit das Feld der personalisierten Medizin voranzubringen. Um klinische Anwendbarkeit zu erreichen, gilt es in der Zukunft, verbleibende Herausforderungen bei der Datenverfügbarkeit zu bewältigen und unsere Vorhersagen experimentell zu validieren

Genetic variation in human drug-related genes

Background: Variability in drug efficacy and adverse effects are observed in clinical practice. While the extent of genetic variability in classic pharmacokinetic genes is rather well understood, the role of genetic variation in drug targets is typically less studied. Methods: Based on 60,706 human exomes from the ExAC dataset, we performed an in-depth computational analysis of the prevalence of functional variants in 806 drug-related genes, including 628 known drug targets. We further computed the likelihood of 1236 FDA-approved drugs to be affected by functional variants in their targets in the whole ExAC population as well as different geographic sub-populations. Results: We find that most genetic variants in drug-related genes are very rare (f < 0.1%) and thus will likely not be observed in clinical trials. Furthermore, we show that patient risk varies for many drugs and with respect to geographic ancestry. A focused analysis of oncological drug targets indicates that the probability of a patient carrying germline variants in oncological drug targets is, at 44%, high enough to suggest that not only somatic alterations but also germline variants carried over into the tumor genome could affect the response to antineoplastic agents. Conclusions: This study indicates that even though many variants are very rare and thus likely not observed in clinical trials, four in five patients are likely to carry a variant with possibly functional effects in a target for commonly prescribed drugs. Such variants could potentially alter drug efficacy. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0502-5) contains supplementary material, which is available to authorized users