# Identification of predictive biomarkers and dose optimization for camrelizumab combined with apatinib in the treatment of advanced hepatocellular carcinoma: a quantitative systems pharmacology approach

## TL;DR

This paper uses a systems pharmacology model to find biomarkers and optimize doses for a cancer treatment combination.

## Contribution

A QSP model identifies biomarkers and suggests a reduced apatinib dose maintains efficacy in treating hepatocellular carcinoma.

## Key findings

- CD8+/Treg, CD4+/Treg ratios, and MDSC density are predictive biomarkers for treatment response.
- Reducing apatinib to 125 mg maintains therapeutic effects in combination therapy.
- The QSP model enables virtual clinical trials for rapid drug regimen evaluation.

## Abstract

The combination of camrelizumab and apatinib represents a promising treatment strategy for patients with advanced hepatocellular carcinoma (aHCC). However, the specific patient populations that may benefit from this combination therapy, as well as the changes in efficacy after adjusting the medication regimen to avoid serious adverse reactions, remain uncertain.

We employ a quantitative systems pharmacology (QSP) approach to address these significant clinical issues. A QSP model is established by integrating pharmacokinetic data of camrelizumab and apatinib, generating a virtual patient cohort for rapid and reliable virtual clinical studies.

Ultimately, our model identifies the pre-treatment CD8+/Treg ratio, CD4+/Treg ratio, and the density of myeloid-derived suppressor cells (MDSCs) as key predictive biomarkers. Furthermore, through computer-simulated clinical trials, we find that reducing the dose of apatinib in combination therapy to 125 mg can still achieve therapeutic effects comparable to the original dose.

These findings provide valuable insights for future drug development and clinical trial design.