Leveraging quantitative systems pharmacology and artificial intelligence to advance treatment of chronic kidney disease mineral bone disorder.

Chronic kidney disease mineral bone disorder (CKD-MBD) is a complex clinical syndrome responsible for the accelerated cardiovascular mortality seen in individuals afflicted with CKD. Current approaches to therapy have failed to improve clinical outcomes adequately, likely due to targeting surrogate biochemical parameters as articulated by the guideline developer, Kidney Disease: Improving Global Outcomes (KDIGO). We hypothesized that using a Systems Biology Approach combining machine learning with mathematical modeling, we could test a novel approach to therapy targeting the abnormal movement of mineral out of bone and into soft tissue that is characteristic of CKD-MBD. The mathematical model describes the movement of calcium and phosphate between body compartments in response to standard therapeutic agents. The machine-learning technique we applied is reinforcement learning (RL). We compared calcium, phosphate, parathyroid hormone (PTH), and mineral movement out of bone and into soft tissue under four scenarios: standard approach (KDIGO), achievement of KDIGO guidelines using RL (RLKDIGO), targeting abnormal mineral flux (RLFLUX), and combining achievement of KDIGO guidelines with minimization of abnormal mineral flux (RLKDIGOFLUX). We demonstrate through simulations that explicitly targeting abnormal mineral flux significantly decreases abnormal mineral movement compared with standard approach while achieving acceptable biochemical outcomes. These investigations highlight the limitations of current therapeutic targets, primarily secondary hyperparathyroidism, and emphasize the central role of deranged phosphate homeostasis in the genesis of the CKD-MBD syndrome.NEW & NOTEWORTHY Artificial intelligence is a powerful tool for exploration of complex processes but application to clinical syndromes is challenging. Using a mathematical model describing the movement of calcium and phosphate between body compartments combined with machine learning, we show the feasibility of testing alternative goals of therapy for Chronic Kidney Disease Mineral Bone Disorder while maintaining acceptable biochemical outcomes. These simulations demonstrate the potential for using this platform to generate and test hypotheses in silico rapidly, inexpensively, and safely.

Application of artificial intelligence to chronic kidney disease mineral bone disorder.

The global derangement of mineral metabolism that accompanies chronic kidney disease (CKD-MBD) is a major driver of the accelerated mortality for individuals with kidney disease. Advances in the delivery of dialysis, in the composition of phosphate binders, and in the therapies directed towards secondary hyperparathyroidism have failed to improve the cardiovascular event profile in this population. Many obstacles have prevented progress in this field including the incomplete understanding of pathophysiology, the lack of clinical targets for early stages of chronic kidney disease, and the remarkably wide diversity in clinical manifestations. We describe in this review a novel approach to CKD-MBD combining mathematical modelling of biologic processes with machine learning artificial intelligence techniques as a tool for the generation of new hypotheses and for the development of innovative therapeutic approaches to this syndrome. Clinicians need alternative targets of therapy, tools for risk profile assessment, and new therapies to address complications early in the course of disease and to personalize therapy to each individual. The complexity of CKD-MBD suggests that incorporating artificial intelligence techniques into the diagnostic, therapeutic, and research armamentarium could accelerate the achievement of these goals.