A systematic review and meta-analysis of the impacts of germline pharmacogenomics on severe toxicity and symptom burden in adult patients with cancer.

The clinical application of Pharmacogenomics (PGx) has improved patient safety. However, comprehensive PGx testing has not been widely adopted in clinical practice, and significant opportunities exist to further optimize PGx in cancer care. This systematic review and meta-analysis aim to evaluate the safety outcomes of reported PGx-guided strategies (Analysis 1) and identify well-studied emerging pharmacogenomic variants that predict severe toxicity and symptom burden (Analysis 2) in patients with cancer. We searched MEDLINE, EMBASE, CENTRAL, clinicaltrials.gov, and International Clinical Trials Registry Platform from inception to January 2023 for clinical trials or comparative studies evaluating PGx strategies or unconfirmed pharmacogenomic variants. The primary outcomes were severe adverse events (SAE; ≥ grade 3) or symptom burden with pain and vomiting as defined by trial protocols and assessed by trial investigators. We calculated pooled overall relative risk (RR) and 95% confidence interval (95%CI) using random effects models. PROSPERO, registration number CRD42023421277. Of 6811 records screened, six studies were included for Analysis 1, 55 studies for Analysis 2. Meta-analysis 1 (five trials, 1892 participants) showed a lower absolute incidence of SAEs with PGx-guided strategies compared to usual therapy, 16.1% versus 34.0% (RR = 0.72, 95%CI 0.57-0.91, p = 0.006, I2 = 34%). Meta-analyses 2 identified nine medicine(class)-variant pairs of interest across the TYMS, ABCB1, UGT1A1, HLA-DRB1, and OPRM1 genes. Application of PGx significantly reduced rates of SAEs in patients with cancer. Emergent medicine-variant pairs herald further research into the expansion and optimization of PGx to improve systemic anti-cancer and supportive care medicine safety and efficacy.

Virtual twins for model-informed precision dosing of clozapine in patients with treatment-resistant schizophrenia.

Model-informed precision dosing using virtual twins (MIPD-VTs) is an emerging strategy to predict target drug concentrations in clinical practice. Using a high virtualization MIPD-VT approach (Simcyp version 21), we predicted the steady-state clozapine concentration and clozapine dosage range to achieve a target concentration of 350 to 600 ng/mL in hospitalized patients with treatment-resistant schizophrenia (N = 11). We confirmed that high virtualization MIPD-VT can reasonably predict clozapine concentrations in individual patients with a coefficient of determination (R2 ) ranging between 0.29 and 0.60. Importantly, our approach predicted the final dosage range to achieve the desired target clozapine concentrations in 73% of patients. In two thirds of patients treated with fluvoxamine augmentation, steady-state clozapine concentrations were overpredicted two to four-fold. This work supports the application of a high virtualization MIPD-VT approach to inform the titration of clozapine doses in clinical practice. However, refinement is required to improve the prediction of pharmacokinetic drug-drug interactions, particularly with fluvoxamine augmentation.