Due to the lack of treatment options for the genetic disease primary hyperoxaluria (PH), including three subtypes PH1, PH2, and PH3, caused by accumulation of oxalate forming kidney stones, there is an urgent need for the development of a drug therapy aside from siRNA drug lumasiran for patients with PH1. After the recent success of drug therapies based on small interfering RNA (siRNA), nedosiran is currently being developed for the treatment of three types of PH as a siRNA-based modality. Through specific inhibition of lactate dehydrogenase enzyme, the key enzyme in biosynthesis of oxalate in liver, phase 1, 2, and 3 clinical trials of nedosiran have achieved the desired primary end point of reduction of urinary oxalate levels in patients with PH1. More PH2 and PH3 patients need to be tested for efficacy. It has also produced a favorable secondary end point on safety and toxicity in PH patients. In addition to common injection site reactions that resolved spontaneously, no severe nedosiran treatment-associated adverse events were reported. Based on the positive results in the clinical studies, nedosiran is a candidate siRNA drug to treat PH patients.
The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using signal flow analysis and feedback vertex set control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task.
The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. NETISCE identifies reprogramming targets through the innovative use of control theory within a dynamical systems framework. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system that are relevant for the desired reprogramming task.
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