A Minimal PBPK/PD Model with Expansion-Enhanced Target-Mediated Drug Disposition to Support a First-in-Human Clinical Study Design for a FLT3L-Fc Molecule.

FLT3L-Fc is a half-life extended, effectorless Fc-fusion of the native human FLT3-ligand. In cynomolgus monkeys, treatment with FLT3L-Fc leads to a complex pharmacokinetic/pharmacodynamic (PK/PD) relationship, with observed nonlinear PK and expansion of different immune cell types across different dose levels. A minimal physiologically based PK/PD model with expansion-enhanced target-mediated drug disposition (TMDD) was developed to integrate the molecule's mechanism of action, as well as the complex preclinical and clinical PK/PD data, to support the preclinical-to-clinical translation of FLT3L-Fc. In addition to the preclinical PK data of FLT3L-Fc in cynomolgus monkeys, clinical PK and PD data from other FLT3-agonist molecules (GS-3583 and CDX-301) were used to inform the model and project the expansion profiles of conventional DC1s (cDC1s) and total DCs in peripheral blood. This work constitutes an essential part of our model-informed drug development (MIDD) strategy for clinical development of FLT3L-Fc by projecting PK/PD in healthy volunteers, determining the first-in-human (FIH) dose, and informing the efficacious dose in clinical settings. Model-generated results were incorporated in regulatory filings to support the rationale for the FIH dose selection.

KIFC1: A Reliable Prognostic Biomarker in Rb-positive Triple-negative Breast Cancer Patients Treated With Doxorubicin in Combination With Abemaciclib.

BACKGROUND/AIM: Triple-negative breast cancer (TNBC) prevalence and risk of relapse are greatest in African American (AA) patients. Doxorubicin (DOX) and abemaciclib (ABE) synergism in Rb-positive TNBC cells (MDA-MB-231), and antagonism in Rb-negative TNBC cells (MDA-MB-468) have been previously shown. Here, we assessed Kinesin-like protein 1 (KIFC1) as an ethnic-specific prognostic biomarker of the DOX+ABE combination for the Rb-status in TNBC. MATERIALS AND METHODS: Literature search for TNBC prognostic biomarkers in the AA population was conducted. MDA-MB-231 and MDA-MB-468 cells were exposed over 72 h to four treatment arms: 1) control (medium without drug), 2) DOX at 50% inhibitory concentration in MDA-MB-231 (0.565 µM) and MDA-MB-468 (0.121 µM), 3) ABE alone (2 µM), and 4) DOX+ABE combination at their corresponding concentrations in each cell-line. KIFC1 protein expression and temporal changes were quantified in MDA-MB-231 cells using western blot. RESULTS: KIFC1, Kaiso, and Annexin A2 are literature-identified AA-specific TNBC prognostic biomarkers. KIFC1 was found to be uncorrelated to other proposed biomarkers, suggesting it may predict risk independently of other TNBC biomarkers. In both cell lines, DOX alone did not significantly change KIFC1 expression relative to control. Conversely, ABE reduced KIFC1 expression in MDA-MB-231 but not in MDA-MB-468 cells. The combination DOX+ABE resulted in a greatest reduction in KIFC1 in MDA-MB-231 cells with a more rapid time-to-full inhibition of KIFC1 compared to ABE alone. CONCLUSION: Change in KIFC1 expression is primarily driven by ABE in Rb-positive TNBC cells. DOX increases ABE speed to achieve a full inhibition of KIFC1 in Rb-positive, yet, without influencing its expression in Rb-negative TNBC cells.

In vitro to Clinical Translation of Combinatorial Effects of Doxorubicin and Abemaciclib in Rb-Positive Triple Negative Breast Cancer: A Systems-Based Pharmacokinetic/Pharmacodynamic Modeling Approach.

BACKGROUND: Doxorubicin (DOX) and its pegylated liposomal formulation (L_DOX) are the standard of care for triple-negative breast cancer (TNBC). However, resistance to DOX often occurs, motivating the search for alternative treatment approaches. The retinoblastoma protein (Rb) is a potential pharmacological target for TNBC treatment since its expression has been associated with resistance to DOX-based therapy. METHODS: DOX (0.01-20 µM) combination with abemaciclib (ABE, 1-6 µM) was evaluated over 72 hours on Rb-positive (MDA-MB-231) and Rb-negative (MDA-MB-468) TNBC cells. Combination indices (CI) for DOX+ABE were calculated using Compusyn software. The TNBC cell viability time-course and fold-change from the control of phosphorylated-Rb (pRb) protein expression were measured with CCK8-kit and enzyme-linked immunosorbent assay. A cell-based pharmacodynamic (PD) model was developed, where pRb protein dynamics drove cell viability response. Clinical pharmacokinetic (PK) models for DOX, L_DOX, and ABE were developed using data extracted from the literature. After scaling cancer cell growth to clinical TNBC tumor growth, the time-to-tumor progression (TTP) was predicted for human dosing regimens of DOX, ABE, and DOX+ABE. RESULTS: DOX and ABE combinations were synergistic (CI<1) in MDA-MB-231 and antagonistic (CI>1) in MDA-MB-468. The maximum inhibitory effects (Imax) for both drugs were set to one. The drug concentrations producing 50% of Imax for DOX and ABE were 0.565 and 2.31 µM (MDA-MB-231) and 0.121 and 1.61 µM (MDA-MB-468). The first-orders rate constants of abemaciclib absorption (ka) and doxorubicin release from L_DOX (kRel) were estimated at 0.31 and 0.013 h-1. Their linear clearances were 21.7 (ABE) and 32.1 L/h (DOX). The estimated TTP for intravenous DOX (75 mg/m2 every 21 days), intravenous L_DOX (50 mg/m2 every 28 days), and oral ABE (200 mg twice a day) were 125, 31.2, and 8.6 days shorter than drug-free control. The TTP for DOX+ABE and L_DOX+ABE were 312 days and 47.5 days shorter than control, both larger than single-agent DOX, suggesting improved activity with the DOX+ABE combination. CONCLUSION: The developed translational systems-based PK/PD model provides an in vitro-to-clinic modeling platform for DOX+ABE in TNBC. Although model-based simulations suggest improved outcomes with combination over monotherapy, tumor relapse was not prevented with the combination. Hence, DOX+ABE may not be an effective treatment combination for TNBC.

Chloroquine sensitizes MDA-MB-231 cells to osimertinib through autophagy-apoptosis crosstalk pathway.

Background: Triple-negative breast cancer (TNBC) is a breast cancer that tests negative for estrogen receptor (ER), progesterone receptors, and human epidermal growth factor receptors 2 (HER2). It is aggressive and invasive in nature and lacks targeted therapy. Purpose: The EGFR is frequently overexpressed in TNBC, and the EGFR-overexpressing TNBC presumably escapes EGFR inhibitor therapy by upregulating autophagy and inhibiting apoptosis. Methods: To parse the autophagy-apoptosis crosstalk pathway as a potential targeted therapy in TNBC, the activity of an EGFR inhibitor, osimertinib, alone and in combination with an autophagy inhibitor, chloroquine, was examined in EGFR-overexpressing TNBC cell line, MDA-MB-231. The nature of interaction between both drugs at various concentrations was determined by calculating combination indexes (CI) using CompuSyn software. Temporal changes in the expression of the autophagy marker, LC3B-II, and several apoptosis signaling molecules were measured using Western blot and luminex assay with MAGPIX® after exposure to drugs. A synergistic interaction (CI <1) was identified with combinations of 4-6.5 µM osimertinib with 30-75 µM chloroquine. Results: A combination of osimertinib (6 µM) with chloroquine (30 µM) resulted in a 6-fold increase of LC3B-II relative to control compared to 2.5-fold increase for either drug alone. The caspase-3 expression increased 2-fold compared to a 0.5-fold decrease with chloroquine and 1.5-fold increase with osimertinib. Conclusion: Our results indicate that inhibition of the autophagic flux via chloroquine improves the effectiveness of osimertinib in TNBC cancer cells, warranting further investigations of this combination in vivo.

A retrospective examination of the US Food and Drug Administration's clinical pharmacology reviews of oncology biologics for potential use of therapeutic drug monitoring.

BACKGROUND: Biologics have gained traction for use in oncology, but have demonstrate clinical variability for efficacy and safety. Therapeutic drug monitoring (TDM) can benefit patients' outcomes from a biologic therapy when the latter has a defined therapeutic window. A clinically relevant therapeutic window may exist for biologics with established exposure-response (E-R) relationships for efficacy and/or safety and a documented maximum tolerated dose (MTD). Additionally, the inter-individual variability (IIV) on the clearance (CL) parameter could determine risks for patients falling outside the proposed therapeutic window. MATERIALS AND METHODS: The US Food and Drug Administration (FDA)-approved oncology biologics between 2005-2016 were reviewed via FDA "Purple Book" (FDA-repository for licensed biologics). Data were extracted from biologics' pharmacokinetic models available on the clinical pharmacology reviews published on the FDA-Approved Drug Products website. Evaluated features for biologics with established E-R relationships for efficacy and/or safety and MTD include an IIV for the CL and various other covariates including demographic factors, disease factors, blood chemistry, or immunogenicity. RESULTS: Five therapies were identified with documented E-R relationships for both efficacy and safety including, Yervoy®(ipilimumab), Zaltrap® (ziv-aflibercept), Portrazza® (necitumumab), Adcetris® (brentuximab-vedotin), and Blincyto® (blinatumomab). The corresponding IIV on CL were: 34%, 33%, 29%, 47%, and 97%, respectively. Among the five therapies, only three had defined MTD including, brentuximab-vedotin, necitumumab, and blinatumomab. CONCLUSION: Of the medications examined, blinatumomab was identified as the anticancer drug with the most available information for the establishment of TDM, and hence, may benefit through the use of TDM to optimize effectiveness and minimize patients' toxicity. The approach used here may provide a generalizable framework to retrospectively identify anticancer biologics with high IIV that may benefit from TDM to improve patients' clinical outcome.

Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer.

Breast cancer (BC) is the most common cancer in women, and the second most frequent cause of cancer-related deaths in women worldwide. It is a heterogeneous disease composed of multiple subtypes with distinct morphologies and clinical implications. Quantitative systems pharmacology (QSP) is an emerging discipline bridging systems biology with pharmacokinetics (PK) and pharmacodynamics (PD) leveraging the systematic understanding of drugs' efficacy and toxicity. Despite numerous challenges in applying computational methodologies for QSP and mechanism-based PK/PD models to biological, physiological, and pharmacological data, bridging these disciplines has the potential to enhance our understanding of complex disease systems such as BC. In QSP/PK/PD models, various sources of data are combined including large, multi-scale experimental data such as -omics (i.e. genomics, transcriptomics, proteomics, and metabolomics), biomarkers (circulating and bound), PK, and PD endpoints. This offers a means for a translational application from pre-clinical mathematical models to patients, bridging the bench to bedside paradigm. Not only can these models be applied to inform and advance BC drug development, but they also could aid in optimizing combination therapies and rational dosing regimens for BC patients. Here, we review the current literature pertaining to the application of QSP and pharmacometrics-based pharmacotherapy in BC including bottom-up and top-down modeling approaches. Bottom-up modeling approaches employ mechanistic signal transduction pathways to predict the behavior of a biological system. The ones that are addressed in this review include signal transduction and homeostatic feedback modeling approaches. Alternatively, top-down modeling techniques are bioinformatics reconstruction techniques that infer static connections between molecules that make up a biological network and include (1) Bayesian networks, (2) co-expression networks, and (3) module-based approaches. This review also addresses novel techniques which utilize the principles of systems biology, synthetic lethality and tumor priming, both of which are discussed in relationship to novel drug targets and existing BC therapies. By utilizing QSP approaches, clinicians may develop a platform for improved dose individualization for subpopulation of BC patients, strengthen rationale in treatment designs, and explore mechanism elucidation for improving future treatments in BC medicine.

Current advances in biomarkers for targeted therapy in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a complex heterogeneous disease characterized by the absence of three hallmark receptors: human epidermal growth factor receptor 2, estrogen receptor, and progesterone receptor. Compared to other breast cancer subtypes, TNBC is more aggressive, has a higher prevalence in African-Americans, and more frequently affects younger patients. Currently, TNBC lacks clinically accepted targets for tailored therapy, warranting the need for candidate biomarkers. BiomarkerBase, an online platform used to find biomarkers reported in clinical trials, was utilized to screen all potential biomarkers for TNBC and select only the ones registered in completed TNBC trials through clinicaltrials.gov. The selected candidate biomarkers were classified as surrogate, prognostic, predictive, or pharmacodynamic (PD) and organized by location in the blood, on the cell surface, in the cytoplasm, or in the nucleus. Blood biomarkers include vascular endothelial growth factor/vascular endothelial growth factor receptor and interleukin-8 (IL-8); cell surface biomarkers include EGFR, insulin-like growth factor binding protein, c-Kit, c-Met, and PD-L1; cytoplasm biomarkers include PIK3CA, pAKT/S6/p4E-BP1, PTEN, ALDH1, and the PIK3CA/AKT/mTOR-related metabolites; and nucleus biomarkers include BRCA1, the gluco-corticoid receptor, TP53, and Ki67. Candidate biomarkers were further organized into a "cellular protein network" that demonstrates potential connectivity. This review provides an inventory and reference point for promising biomarkers for breakthrough targeted therapies in TNBC.

Ingredients in fruit juices interact with dasatinib through inhibition of BCRP: a new mechanism of beverage-drug interaction.

Small molecule tyrosine kinase inhibitors (TKIs) are a group of highly novel and target-specific anticancer drugs. Recently, most TKIs are found to be substrates of P-glycoprotein (P-gp) and Breast Cancer Resistance Protein (BCRP). However, little information is available regarding the Pgp- or BCRP-mediated interaction of TKIs with coadministered drugs/food/beverage. Our objective was to evaluate the effect of the major ingredients of grapefruit juice (GFJ), orange juice (OJ), apple juice (AJ), and green tea on P-gp and BCRP-mediated dasatinib efflux. Among the 14 ingredients screened, only tangeretin and nobiletin moderately inhibited P-gp-mediated dasatinib efflux. In contrast, four ingredients in GFJ [i.e., bergamottin, 6',7'-dihydroxybergamottin (DHB), quercetin, and kaempferol], two ingredients in OJ (tangeretin and nobiletin), and one ingredient in AJ (i.e., hesperetin) greatly inhibited BCRP-mediated dasatinib efflux at the concentration of 50 µM (p < 0.001). Further concentration-dependent studies revealed that bergamottin, DHB, tangeretin, and nobiletin are potent BCRP inhibitors, with IC50 values 3.19, 5.2, 1.19, and 1.04 µM, respectively. Further in vivo investigations are warranted to evaluate the BCRP-mediated FJ-TKI interaction. Literature reports only documented the modulatory effect of FJ and green tea on CYP3A, P-gp, and OATP. Our novel finding that FJ ingredients strongly inhibit BCRP may represent a new mechanism of beverage-drug interaction.