Jeannine S. McCune

The optimal post‐transplant cyclophosphamide (PTCy) dose to prevent graft‐versus‐host disease (GVHD) after allogeneic hematopoietic cell transplant (HCT) is undefined. Data from a novel murine HLA‐haploidentical HCT model suggested that PTCy has a dose‐dependent effect associated with its efficacy in preventing GVHD, including reduced proliferation of conventional CD4 ⁺ Foxp3 ⁻ T‐helper cells (Th) at Day 7, followed by the preferential expansion of regulatory CD4 ⁺ CD25 ⁺ Foxp3 ⁺ T cells (Tregs) at Day 21 after HCT. To better quantify the impact of the PTCy dose, we built a hybrid quantitative systems pharmacology (hybrid systems) model consisting of: (1) simulated pharmacokinetics of PTCy and its metabolites; (2) the donor immune system; and (3) a neural network mapping drug exposure and T‐cell profiles to clinical scores of acute GVHD. Data were collected from murine MHC‐haploidentical HCT studies of PTCy from 0 to 100 mg/kg/day on Days 3 and 4 after transplantation of donor cells. The final model successfully captured time‐varying changes in donor Tcytotoxic CD8 ⁺ T cells (Tc), Th, and Tregs up to Day 21. The clinical score for GVHD was associated with numerous descriptors, with the immediately preceding GVHD score, body weight, PTCy dose, and donor Treg concentration in the blood and in the liver having the greatest contribution. This hybrid systems model quantitatively captures the beneficial impact of PTCy on Tc, Th, Tregs, and acute GVHD after HCT. The mediation through PTCy and metabolite concentrations and Tc, Th, and Treg numbers is underlined, and the model may be further used to refine and improve PTCy regimens.

The inflammatory cytokine interleukin‐6 (IL‐6) plays a significant role in the progression of cardiovascular disease (CVD). We evaluated the impact of clazakizumab, a monoclonal antibody targeting the IL‐6 ligand, upon high‐sensitivity C‐reactive protein (hs‐CRP) as part of the dose‐finding phase 2b trial. In this randomized, double‐blind trial, adults with cardiovascular disease and/or diabetes receiving maintenance dialysis and having hs‐CRP ≥ 2 mg/L at baseline received clazakizumab (2.5 mg, 5 mg, or 10 mg, n = 32 per dose group) or placebo ( n = 31) every 4 weeks. These data were used to develop a population pharmacokinetics (popPK) and a longitudinal PK‐pharmacodynamic (PK‐PD) model to characterize the relationship between clazakizumab and hs‐CRP to support Phase 3 dose selection. A 2‐compartment model with linear elimination well described clazakizumab PK. PK‐PD was best described by an indirect‐response inhibitory model, where clazakizumab concentrations inhibited the k _in (zero‐order rate of production) of hs‐CRP. Simulations predicted that all doses (2.5, 5, and 10 mg IV every 4 weeks) would result in the majority (> 80%) of subjects experiencing at least an 80% decrease in baseline hs‐CRP (80.9%, 83.4%, and 88.1%, respectively). In addition, the proportion of subjects with hs‐CRP concentrations below 2 mg/L at Week 12 was predicted to be 67.7%, 76.7%, and 82.6%, respectively, for these 3 dose groups. These modeling and simulation results support the dose selection of clazakizumab 5 mg IV every 4 weeks in the phase 3 placebo‐controlled trial to evaluate the impact of IL‐6 inhibition via clazakizumab administration upon cardiovascular events.

Wearable and implantable biosensors are pioneering new frontiers in precision medicine by enabling continuous biomolecule analysis for fundamental investigation and personalized health monitoring. However, their widespread adoption remains impeded by challenges such as the limited number of detectable targets, operational instability and production scalability. Here, to address these issues, we introduce printable core–shell nanoparticles with built-in dual functionality: a molecularly imprinted polymer shell for customizable target recognition, and a nickel hexacyanoferrate core for stable electrochemical transduction. Using inkjet printing with an optimized nanoparticle ink formulation, we demonstrate the mass production of robust and flexible biosensors capable of continuously monitoring a broad spectrum of biomarkers, including amino acids, vitamins, metabolites and drugs. We demonstrate their effectiveness in wearable metabolic monitoring of vitamin C, tryptophan and creatinine in individuals with long COVID. Additionally, we validate their utility in therapeutic drug monitoring for cancer patients and in a mouse model through providing real-time analysis of immunosuppressants such as busulfan, cyclophosphamide and mycophenolic acid.

Dose optimization of sirolimus may further improve outcomes in allogeneic hematopoietic cell transplant (HCT) patients receiving post‐transplantation cyclophosphamide (PTCy) to prevent graft‐versus‐host disease (GVHD). Sirolimus exposure–response association studies in HCT patients (i.e., the association of trough concentration with clinical outcomes) have been conflicting. Sirolimus has important effects on T‐cells, including conventional (Tcons) and regulatory T‐cells (Tregs), both of which have been implicated in the mechanisms by which PTCy prevents GVHD, but there is an absence of validated biomarkers of sirolimus effects on these cell subsets. Considering the paucity of existing biomarkers and the complexities of the immune system, we conducted a literature review to inform a quantitative systems pharmacology (QSP) model of GVHD. The published literature presented multiple challenges. The sirolimus pharmacokinetic models insufficiently describe sirolimus distribution to relevant physiological compartments. Despite multiple publications describing sirolimus effects on Tcons and Tregs in preclinical and human ex vivo models, consistent parameters relating sirolimus concentrations to circulating Tcons and Tregs could not be found. Each aspect presents a challenge in building a QSP model of sirolimus and its temporal effects on T‐cell subsets and GVHD prevention. To optimize GVHD prevention regimens, phase I studies and systematic studies of immunosuppressant concentration–effect association are needed for QSP modeling.

Purpose 4-hydroxycyclophosphamide (4HCY) is the principal precursor to the cytotoxic metabolite of cyclophosphamide (CY), which is often used as first-line treatment of children with cancer. There is conflicting data regarding the relationship between CY efficacy, toxicity, and pharmacokinetics with the genes encoding proteins involved in 4HCY pharmacokinetics, specifically its formation and elimination. Methods We evaluated germline pharmacogenetics in children with various malignancies receiving their first CY dose. Using linear regression, we analyzed the associations between two pharmacokinetic outcomes – how fast a child cleared CY (i.e., CY clearance) and the ratio of the 4HCY/CY exposure, specifically area under the plasma concentration-time curve (AUC), and 372 single nucleotide polymorphisms (SNP) in 14 drug-metabolizing transporters or enzymes involved in 4HCY formation or elimination. Results Age was associated with the ratio of 4HCY/CY AUC (P = 0.004); Chemotherapy regimen was associated with CY clearance (P = 0.003). No SNPs were associated with CY clearance or the ratio of 4HCY/CY AUC after controlling for a false discovery rate. Conclusion Age and chemotherapy regimen, but not germline pharmacogenomics, were associated with CY clearance or the ratio of 4HCY/CY AUC. Other methods, such as metabolomics or lipidomics, should be explored.

Supplementary Table S1-S14 from Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

Supplementary Table S1-S14 from Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

Purpose:The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community.Experimental Design:We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML. Statistical approaches and machine learning models were applied to identify signatures for drug response prediction. We also integrated large public datasets to understand the co-occurring mutation patterns and further investigated the mutation profiles in the single cells. The features revealed in the co-occurring or mutual exclusivity pattern were further subjected to machine learning models.Results:We detected genetic signatures associated with sensitivity or resistance to specific agents, and identified five co-occurring mutation groups. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct subclones within patients with AML. Using the mutation pattern for drug response prediction demonstrates high accuracy in predicting sensitivity to some drug classes, such as MEK inhibitors for RAS-mutated leukemia.Conclusions:Our study highlights the importance of considering the gene mutation patterns for the prediction of drug response in AML. It provides a framework for categorizing patients with AML by mutations that enable drug sensitivity prediction.