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de Wijn, A. S.

Publications and source records attributed to de Wijn, A. S..

2 recordsLinked to original sources

A computational dynamic model of combination treatment for type II inhibitors with asciminib

Despite continuous strides forward in drug development, resistance to treatment looms large in the battle against cancer as well as communicable diseases. Chronic myeloid leukaemia (CML) is treated with targeted therapy and treatment is personalised when resistance arises. It has been extensively studied and is used as a model for targeted therapy. In this work, we examine combination treatments of type II Abl1 inhibitors and asciminib (an allosteric regulator) through a computational model at patient relevant concentrations. Due to the separate binding sites of type II inhibitors and asciminib, we propose their combination treatment as potentially robust to resistance. We find that the simultaneous cobinding of type II inhibitors and asciminib is high in synergetic combinations. As an aid to designing and comparing combination treatments, we put forward an equation that expands on the effective ratio of IC50 (ERIC). Unlike usual comparisons of IC50 values, ERIC takes takes patient plasma concentrations into account. The product of two ERIC values (ERICcombo) creates comparable approximations of the effectiveness of combination treatments with low levels of synergy or antagonism at different concentrations. Its simple formulation is done without experiments and requires less computation and input data than the current standard of ZIP values. As such, the new scheme is a useful complement to experiments that deal with synergy in drug use.

cancer biology↗

Beyond IC50 - A computational dynamic model of drug resistance in enzyme inhibition treatment.

Resistance to therapy is a major clinical obstacle to treatment of cancer and communicable diseases. Chronic myeloid leukaemia (CML) is a blood cancer that is treated with Abl1 inhibitors, and is often seen as a model for targeted therapy and drug resistance. Resistance to the first-line treatment occurs in approximately one in four patients. The most common cause of resistance is mutations in the Abl1 enzyme. Different mutant Abl1 enzymes show resistance to different Abl1 inhibitors and the mechanisms that lead to resistance for various mutation and inhibitor combinations are not fully known, making the selection of Abl1 inhibitors for treatment a difficult task. We developed a model based on information of catalysis, inhibition and pharmacokinetics, and applied it to study the effect of three Abl1 inhibitors on mutants of the Abl1 enzyme. From this model, we show that the relative decrease of product formation rate (defined in this work as "inhibitory reduction prowess") is a better indicator of resistance than an examination of the size of the product formation rate or fold-IC50 values for the mutant. We also examine current ideas and practices that guide treatment choice and suggest a new parameter for selecting treatments that could increase the efficacy and thus have a positive impact on patient outcomes.

biophysics↗