RESUMO
Dexrazoxane (DEX) is the only drug clinically approved to treat Doxorubicin-induced cardiotoxicity (DIC), however its impact on the anticancer efficacy of DOX is not extensively studied. In this manuscript, a proof-of-concept in vitro study is carried out to quantitatively characterize the anticancer effects of DOX and DEX and determine their nature of drug-drug interactions in cancer cells by combining experimental data with modeling approaches. First, we determined the static concentration-response of DOX and DEX in breast cancer cell lines, JIMT-1 and MDA-MB-468. With a three-dimensional (3D) response surface analysis using a competitive interaction model, we characterized their interaction to be modestly synergistic in MDA-MB-468 or modestly antagonistic in JIMT-1 cells. Second, a cellular-level, pharmacodynamic (PD) model was developed to capture the time-course effects of the two drugs which determined additive and antagonistic interactions for DOX and DEX in MDA-MB-468 and JIMT-1, respectively. Finally, we performed in vitro to in vivo translation by utilizing DOX and DEX clinical dosing regimen that was previously identified to be maximally cardioprotective, to drive tumor cell PD models. The resulting simulations showed that a 10:1 DEX:DOX dose ratio over three cycles of Q3W regimen of DOX results in comparable efficacy based on MDA-MB-468 (additive effect) estimates and lower efficacy based on JIMT-1 (antagonistic effect) estimates for DOX + DEX combination as compared to DOX alone. Thus, our developed cell-based PD models can be used to simulate different scenarios and better design preclinical in vivo studies to further optimize DOX and DEX combinations.
RESUMO
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.
RESUMO
A variety of marketed drugs belonging to various therapeutic classes are known to cause nephrotoxicity. Nephrotoxicity can manifest itself in several forms depending on the specific site involved as well as the underlying pathophysiological mechanisms. As they often coexist with other pathophysiological conditions, the steps that can be taken to treat them are often limited. Thus, drug-induced nephrotoxicity remains a major clinical challenge. Prior knowledge of risk factors associated with special patient populations and specific classes of drugs, combined with early diagnosis, therapeutic drug monitoring with dose adjustments, as well as timely prospective treatments are essential to prevent and manage them better. Most incident drug-induced renal toxicity is reversible only if diagnosed at an early stage and treated promptly. Hence, diagnosis at an early stage is the need of the hour to counter it. Significant recent advances in the identification of novel early biomarkers of nephrotoxicity are not beyond limitations. In such a scenario, mathematical modeling and simulation (M&S) approaches may help to better understand and predict toxicities in a clinical setting. This review summarizes pathophysiological mechanisms of drug-induced nephrotoxicity, classes of nephrotoxic drugs, management, prevention, and diagnosis in clinics. Finally, it also highlights some of the recent advancements in mathematical M&S approaches that could be used to better understand and predict drug-induced nephrotoxicity.
