Emulator-based Bayesian calibration of the CISNET colorectal cancer models

Purpose

To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET) 's SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.

Methods

We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANN) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.

Results

The optimal ANN for SimCRC had four hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had one hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 hours for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.

Conclusions

Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, like the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating three realistic CRC individual-level models using a Bayesian approach.

Effect of intact oxaliplatin in plasma on a cold allodynia after multiple administrations in colorectal cancer model rats

BACKGROUND

Oxaliplatin (L-OHP)-induced acute neuropathy is a major factor for influencing treatment compliance in patients receiving chemotherapy for colorectal cancer (CRC). Acute neuropathy is caused by the intact L-OHP affecting the function of transient receptor potential vanilloid 1 (TRPV1) channel. In this study, the effectiveness of the detection of the intact L-OHP and the association with the severity of L-OHP-induced acute neuropathy were investigated using a rat model of CRC.

METHODS

Wistar male rats were prepared as models of CRC by using 1,2-dimethylhydrazine (DMH) and dextran sulfate. Intravenous L-OHP was administered (weekly) to CRC rats for 4 weeks, at doses of 3, 5, or 8 mg/kg, respectively. Pharmacokinetic and tumor distribution profiles of animals with intact L-OHP were observed on days 1 and 22. Cold allodynia was determined as a read-out of acute neuropathy using the acetone test over the 4 weeks.

RESULTS

The mean AUC0-∞ values for the intact L-OHP were increased dose-dependently at the three doses. The accumulation of intact L-OHP was confirmed following an increase in L-OHP concentration on day 22. Acute neuropathy was observed from day 2 at all doses and the withdrawal response correlated with AUC (R2 =0.9816).

CONCLUSIONS

To prevent the onset of L-OHP-induced acute neuropathy, the timely detection of the intact L-OHP is important. These findings could be a basis of the establishment a pharmacokinetic and toxicodynamic model to aid the planning of therapy cycle completion for CRC.