Periodic Reporting for period 4 - DC-ren (Drug combinations for rewriting trajectories of renal pathologies in type II diabetes (DC-ren))
Periodo di rendicontazione: 2024-07-01 al 2024-12-31
Diabetic Kidney Disease (DKD) is a serious condition with a dramatically increasing prevalence. The clinical course is currently captured by stages, defined by urinary albumine excretion and estimated glomerular filtration rate. This approach allows an approximation of prognosis for cohorts, but definitely lacks accuracy on the level of individuals. Stages also guide treatment, and drugs for controlling various risk factors are at hand. On top, novel drugs demonstrating benefit have been introduced to the clinics recently. However, while proving effective on a cohort level, drug and drug combination therapy see considerable variance in response on the level of individuals. Analysis of individual patients with DKD identifies inter-, but also intra-individual (longitudinal) heterogeneity in disease evolution (prognosis) and drug response. Both are the direct consequence of an interaction between prevailing pathophysiology and drug mechanism of action. Hence, variability in prognosis and drug response reflect variability in pathophysiology. Accordingly, personalized DKD treatment demands improved patient phenotyping.
DC-ren followed a state-transition concept of disease evolution (a DKD state map):
A combination of genetic predisposition and environmental factors leads to individualized entry paths into DKD (triggering inter-individual heterogeneity). Further disease evolution follows changing states of pathophysiology (reflected as intra-individual variability) that are (or are not) amendable by drugs via their specific molecular mode of action. Assignment of individual patients to specific states via targeted phenotyping thus offers improved estimation of prognosis and informs on respective pathophysiology, allowing rational selection of drugs and drug combinations on the level of individual patients irrespective of disease stage.
The central objective of DC-ren was to transfer the state map concept into a software solution, the DC-ren toolbox, tailored at decision support for predictive statements allowing targeted therapy. This toolbox was evaluated in a (virtual) clinical trial to answer the question if a DKD patient presenting at the clinic shall stay on current therapy, or change to a different drug combination for stabilization of kidney function.
DC-ren followed a state-transition concept of disease evolution (a DKD state map):
A combination of genetic predisposition and environmental factors leads to individualized entry paths into DKD (triggering inter-individual heterogeneity). Further disease evolution follows changing states of pathophysiology (reflected as intra-individual variability) that are (or are not) amendable by drugs via their specific molecular mode of action. Assignment of individual patients to specific states via targeted phenotyping thus offers improved estimation of prognosis and informs on respective pathophysiology, allowing rational selection of drugs and drug combinations on the level of individual patients irrespective of disease stage.
The central objective of DC-ren was to transfer the state map concept into a software solution, the DC-ren toolbox, tailored at decision support for predictive statements allowing targeted therapy. This toolbox was evaluated in a (virtual) clinical trial to answer the question if a DKD patient presenting at the clinic shall stay on current therapy, or change to a different drug combination for stabilization of kidney function.
We established the DC-ren data space, defining kidney relevant process models and embedded biomarker candidates linked with disease progression and drug mechanism of action, and developed two analytic concepts. One focused on prediction of future outcome based on current information (state map approach), while the other followed changes in variables over time that are directly predictive of outcome (state evolution map approach). These efforts were complemented by efforts to make predictive statements by analysis of the urinary proteome. The algorithms approximating the observed variance of eGFR trajectories in the context of treatments of interest (renin angiotensin system blocker only or in combination with SGLT2 inhibitors, mineralocorticoid receptor antagonists or GLP1 agonists) were developed and tested for their sensitivity and specificity in a discovery cohort.
Next DKD mechanisms and their interactions to rationalize the predictive statements for the response to specific treatments were determined. A decision-support toolbox software prototype was developed and a protocol of a virtual clinical validation trial was designed. A systematic literature review was performed to assess the current accuracy of treatment response prediction to be used as a benchmark for the DC-ren results.
The validation trial showed that sensitivity and specificity of the various approaches to predict short term outcome are in line with currently available assessments of long term outcomes, but not superior. Furthermore, it became clear that conceptually both state map and state evolution map algorithms are necessary to capture both organ specific damage programmes and external factors also affecting outcome.
In addition, several application settings were developed (open drug repositioning, cost benefit analysis, patient and expert surveys, design of a clinical trial to test the technical feasability of decision support based clinical routine). Finally the generalizability of the state maps and state evolutions maps in complex diseases beyond DKD and an in-silico workflow for drug response prediction utilizing urinary proteomics profiles were elaborated.
In summary DC-ren has reached its goals but clearly additional work will be necessary to obtain a deterministic tool that can be transferred into routine clinical use. Technically we established two frameworks with associated workflows how conventional staging systems for complex diseases can be replaced by states. Analysis of the results point towards the necessity to combine concepts to capture both, organ specific events as well as interfering external factors. Finally, DC-ren developed a toolbox that is flexible and thus able to host different analysis tools well beyond those that are established within the project run time.
Exploitation beyond project runtime will focus on the following assets:
• an in-silico workflow for drug response prediction utilizing urinary proteomics profiles
• a computational workflow for mechanism-guided time series analysis
• an integrated learning workflow for estimating personalized drug response
• a software toolbox for integrating technically heterogeneous classification functions
• a web-based tool for analysis of ncRNAs in disease phenotypes
For dissemination, project results were presented at national and international meetings and published in the scientific literature, in lay press and media. The DC ren webpage is updated regularly, also holding videos of project partners detailing their contributions. Outreach was further increased via social media presence.
Next DKD mechanisms and their interactions to rationalize the predictive statements for the response to specific treatments were determined. A decision-support toolbox software prototype was developed and a protocol of a virtual clinical validation trial was designed. A systematic literature review was performed to assess the current accuracy of treatment response prediction to be used as a benchmark for the DC-ren results.
The validation trial showed that sensitivity and specificity of the various approaches to predict short term outcome are in line with currently available assessments of long term outcomes, but not superior. Furthermore, it became clear that conceptually both state map and state evolution map algorithms are necessary to capture both organ specific damage programmes and external factors also affecting outcome.
In addition, several application settings were developed (open drug repositioning, cost benefit analysis, patient and expert surveys, design of a clinical trial to test the technical feasability of decision support based clinical routine). Finally the generalizability of the state maps and state evolutions maps in complex diseases beyond DKD and an in-silico workflow for drug response prediction utilizing urinary proteomics profiles were elaborated.
In summary DC-ren has reached its goals but clearly additional work will be necessary to obtain a deterministic tool that can be transferred into routine clinical use. Technically we established two frameworks with associated workflows how conventional staging systems for complex diseases can be replaced by states. Analysis of the results point towards the necessity to combine concepts to capture both, organ specific events as well as interfering external factors. Finally, DC-ren developed a toolbox that is flexible and thus able to host different analysis tools well beyond those that are established within the project run time.
Exploitation beyond project runtime will focus on the following assets:
• an in-silico workflow for drug response prediction utilizing urinary proteomics profiles
• a computational workflow for mechanism-guided time series analysis
• an integrated learning workflow for estimating personalized drug response
• a software toolbox for integrating technically heterogeneous classification functions
• a web-based tool for analysis of ncRNAs in disease phenotypes
For dissemination, project results were presented at national and international meetings and published in the scientific literature, in lay press and media. The DC ren webpage is updated regularly, also holding videos of project partners detailing their contributions. Outreach was further increased via social media presence.
Cohort-centric approaches have been the gold standard for improving disease diagnosis as well as drug development, testing and prescription. According to stratification means homogeneous cohorts are defined, followed by one-size-fits-all prognosis and treatment guidelines using the stratification criteria as basis. Depending on the nature of the disease such approaches naturally see limits. Specifically for complex, age-associated disorders with multiple treatment approaches available improved stratification is needed, and cross-sectional segmentation has become a widely accepted strategy. However, cross-sectional segmentation may not be sufficient. Even individuals assigned to subgroups with improved cross-sectional homogeneity may still see episodes of slow and fast disease progression, and periods with excellent but also with limited response to a certain drug/drug combinations.
Correct stratification of patients demands predictive biomarkers serving as proxy for
i) molecular mechanistic aspects of disease evolution and
ii) informing on effect of drug mechanism of action.
State maps holding these markers promise to serve the task and DC-ren developed and exemplified this approach for DKD, a complication seen with T2DM and concomitant significant socio-economic impact. Methods development have been integrated into a software prototype solution with the promise to contribute to the wider area of personalization strategies and precision medicine.
Correct stratification of patients demands predictive biomarkers serving as proxy for
i) molecular mechanistic aspects of disease evolution and
ii) informing on effect of drug mechanism of action.
State maps holding these markers promise to serve the task and DC-ren developed and exemplified this approach for DKD, a complication seen with T2DM and concomitant significant socio-economic impact. Methods development have been integrated into a software prototype solution with the promise to contribute to the wider area of personalization strategies and precision medicine.