Pharmacokinetic/Pharmacodynamic Modeling of the Anti-Cancer Effect of Dexamethasone in Pancreatic Cancer Xenografts and Anticipation of Human Efficacious Doses

Cancer Patient-Derived Cell-Based Models: Applications and Challenges in Functional Precision Medicine

The field of oncology has witnessed remarkable progress in personalized cancer therapy. Functional precision medicine has emerged as a promising avenue for achieving superior treatment outcomes by integrating omics profiling and sensitivity testing of patient-derived cancer cells. This review paper provides an in-depth analysis of the evolution of cancer-directed drugs, resistance mechanisms, and the role of functional precision medicine platforms in revolutionizing individualized treatment strategies. Using two-dimensional (2D) and three-dimensional (3D) cell cultures, patient-derived xenograft (PDX) models, and advanced functional assays has significantly improved our understanding of tumor behavior and drug response. This progress will lead to identifying more effective treatments for more patients. Considering the limited eligibility of patients based on a genome-targeted approach for receiving targeted therapy, functional precision medicine provides unprecedented opportunities for customizing medical interventions according to individual patient traits and individual drug responses. This review delineates the current landscape, explores limitations, and presents future perspectives to inspire ongoing advancements in functional precision medicine for personalized cancer therapy.

An integrated PK/PD model investigating the impact of tumor size and systemic safety on animal survival in SW1990 pancreatic cancer xenograft

Survival is one of the most important endpoints in cancer therapy, and parametric survival analysis could comprehensively reveal the overall result of disease progression, drug efficacy, toxicity as well as their interactions. In this study we investigated the efficacy and toxicity of dexamethasone (DEX) combined with gemcitabine (GEM) in pancreatic cancer xenograft. Nude mice bearing SW1990 pancreatic cancer cells derived tumor were treated with DEX (4 mg/kg, i.g.) and GEM (15 mg/kg, i.v.) alone or in combination repeatedly (QD, Q3D, Q7D) until the death of animal or the end of study. Tumor volumes and net body weight (NBW) were assessed every other day. Taking NBW as a systemic safety indicator, an integrated pharmacokinetic/pharmacodynamic (PK/PD) model was developed to quantitatively describe the impact of tumor size and systemic safety on animal survival. The PK/PD models with time course data for tumor size and NBW were established, respectively, in a sequential manner; a parametric time-to-event (TTE) model was also developed based on the longitudinal PK/PD models to describe the survival results of the SW1990 tumor-bearing mice. These models were evaluated and externally validated. Only the mice with good tumor growth inhibition and relatively stable NBW had an improved survival result after DEX and GEM combination therapy, and the simulations based on the parametric TTE model showed that NBW played more important role in animals' survival compared with tumor size. The established model in this study demonstrates that tumor size was not always the most important reason for cancer-related death, and parametric survival analysis together with safety issues was also important in the evaluation of oncology therapies in preclinical studies.

Personalized Medicine for Classical Anesthesia Drugs and Cancer Progression

In this review, we aim to discuss the use and effect of five different drugs used in the induction of anesthesia in cancer patients. Propofol, fentanyl, rocuronium, sugammadex, and dexamethasone are commonly used to induce anesthesia and prevent pain during surgery. Currently, the mechanisms of these drugs to induce the state of anesthesia are not yet fully understood, despite their use being considered safe. An association between anesthetic agents and cancer progression has been determined; therefore, it is essential to recognize the effects of all agents during cancer treatment and to evaluate whether the treatment provided to the patients could be more precise. We also highlight the use of in silico tools to review drug interaction effects and safety, as well as the efficacy of the treatment used according to different subgroups of patients.

Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data

Lung cancer is one of the leading causes of cancer-related death, with a five-year survival rate of 18%. It is a priority for us to understand the underlying mechanisms affecting lung cancer therapeutics' implementation and effectiveness. In this study, we combine the power of Bioinformatics and Systems Biology to comprehensively uncover functional and signaling pathways of drug treatment using bioinformatics inference and multiscale modeling of both scRNA-seq data and proteomics data. Based on a time series of lung adenocarcinoma derived A549 cells after DEX treatment, we first identified the differentially expressed genes (DEGs) in those lung cancer cells. Through the interrogation of regulatory network of those DEGs, we identified key hub genes including TGFβ, MYC, and SMAD3 varied underlie DEX treatment. Further gene set enrichment analysis revealed the TGFβ signaling pathway as the top enriched term. Those genes involved in the TGFβ pathway and their crosstalk with the ERBB pathway presented a strong survival prognosis in clinical lung cancer samples. With the basis of biological validation and literature-based curation, a multiscale model of tumor regulation centered on both TGFβ-induced and ERBB-amplified signaling pathways was developed to characterize the dynamic effects of DEX therapy on lung cancer cells. Our simulation results were well matched to available data of SMAD2, FOXO3, TGFβ1, and TGFβR1 over the time course. Moreover, we provided predictions of different doses to illustrate the trend and therapeutic potential of DEX treatment. The innovative and cross-disciplinary approach can be further applied to other computational studies in tumorigenesis and oncotherapy. We released the approach as a user-friendly tool named BIMM (Bioinformatic Inference and Multiscale Modeling), with all the key features available at https://github.com/chenm19/BIMM.