Despite exten­sive research in person­al­ized medicine, promis­ing person­al­ized thera­pies still fail to trans­late into clini­cal practice. In my research project, I aim to construct a pathway model that predicts the effects of poten­tial thera­pies by combin­ing mecha­nis­tic model­ing and exper­i­men­tal approaches to meet ideal crite­ria for facil­i­tat­ing the trans­la­tion of research to patients.

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In person­al­ized medicine, the under­ly­ing cause of a disease is precisely charac­ter­ized based on specific changes to treat it with an appro­pri­ate targeted therapy. Unfor­tu­nately, success­ful person­al­ized thera­pies do not make the transi­tion to clini­cal practice and remain stuck in scien­tific liter­a­ture, despite inten­sive research in this field.

The bench-to-bedside trans­la­tion process requires consid­er­a­tion of a number of crite­ria, that should be ideally incor­po­rated into the research process, includ­ing provid­ing an answer to an impor­tant clini­cal question, showing a correct outcome classi­fi­ca­tion, enabling standard­iza­tion, and being cost-effec­tive. The mathe­mat­i­cal descrip­tion of disease dynam­ics is ideal to fulfill these crite­ria during the research process, ensur­ing that basic science results do not remain lost in trans­la­tion but find their way to the patient.

In my research project within the Hector Fellow Academy, I plan an inter­dis­ci­pli­nary approach with a combi­na­tion of mathe­mat­i­cal ordinary differ­en­tial equation model­ing, compu­ta­tional analy­sis, and exper­i­men­tal work to create a pathway model that can predict the effect of poten­tial drugs. Network­ing with poten­tial clini­cal collab­o­ra­tors will be a part of my work. With the increased relevance and relia­bil­ity of the
research results gener­ated using the model and its success­ful valida­tion, I would like to highlight that mathe­mat­i­cal model­ing is able to signif­i­cantly enhance the trans­la­tion process.