A pediatric quantitative systems pharmacology model of neurofilament trafficking in spinal muscular atrophy treated with the antisense oligonucleotide nusinersen.

Phosphorylated neurofilament heavy subunit (pNfH) has been recently identified as a promising biomarker of disease onset and treatment efficacy in spinal muscular atrophy (SMA). This study introduces a quantitative systems pharmacology model representing the SMA pediatric scenario in the age range of 0-20 years with and without treatment with the antisense oligonucleotide nusinersen. Physiological changes typical of the pediatric age and the contribution of SMA and its treatment to the peripheral pNfH levels were included in the model by extending the equations of a previously developed mathematical model describing the neurofilament trafficking in healthy adults. All model parameters were estimated by fitting data from clinical trials that enrolled SMA patients treated with nusinersen. The data from the control group of the study was employed to build an in silico population of untreated subjects, and the parameters related to the treatment were estimated by fitting individual pNfH time series of SMA patients followed during the treatment. The final model reproduces well the pNfH levels in the presence of SMA in both the treated and untreated conditions. The results were validated by comparing model predictions with the data obtained from an additional cohort of SMA patients. The reported good predictive model performance makes it a valuable tool for investigating pNfH as a biomarker of disease progression and treatment response in SMA and for the in silico evaluation of novel treatment protocols.

An age-dependent mathematical model of neurofilament trafficking in healthy conditions.

Neurofilaments (Nfs) are the major structural component of neurons. Their role as a potential biomarker of several neurodegenerative diseases has been investigated in past years with promising results. However, even under physiological conditions, little is known about the leaking of Nfs from the neuronal system and their detection in the cerebrospinal fluid (CSF) and blood. This study aimed at developing a mathematical model of Nf transport in healthy subjects in the 20-90 age range. The model was implemented as a set of ordinary differential equations describing the trafficking of Nfs from the nervous system to the periphery. Model parameters were calibrated on typical Nf levels obtained from the literature. An age-dependent function modeled on CSF data was also included and validated on data measured in serum. We computed a global sensitivity analysis of model rates and volumes to identify the most sensitive parameters affecting the model's steady state. Age, Nf synthesis, and degradation rates proved to be relevant for all model variables. Nf levels in the CSF and in blood were observed to be sensitive to the Nf leakage rates from neurons and to the blood clearance rate, and CSF levels were also sensitive to rates representing CSF turnover. An additional parameter perturbation analysis was also performed to investigate possible transient effects on the model variables not captured by the sensitivity analysis. The model provides useful insights into Nf transport and constitutes the basis for implementing quantitative system pharmacology extensions to investigate Nf trafficking in neurodegenerative diseases.

QSPcc reduces bottlenecks in computational model simulations.

Mathematical models have grown in size and complexity becoming often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, these models may be run thousands of times. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here we show a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code. At first QSPcc is demonstrated to be crucial in enabling the research on otherwise intractable Quantitative Systems Pharmacology models, such as in rare Lysosomal Storage Disorders. To demonstrate the full value in seamlessly accelerating, or enabling, the R&D efforts in natural sciences, we then benchmark QSPcc against 8 solutions on 24 real-world projects from different scientific fields. With speed-ups of 22000x peak, and 1605x arithmetic mean, our results show consistent superior performances.