title: Systems pharmacological analysis of paclitaxel-mediated tumor priming that enhances nanocarrier deposition and efficacy.
abstract: Paclitaxel (PAC)-mediated apoptosis decompresses and primes tumors for enhanced deposition of nanoparticulate agents such as pegylated liposomal doxorubicin (DXR). A quantitative pharmacokinetic/pharmacodynamic (PK/PD) approach was developed to analyze efficacy and identify optima for PAC combined with sterically stabilized liposome (SSL)-DXR. Using data extracted from diverse literature sources, Cremophor-paclitaxel (Taxol(®)) PK was described by a carrier-mediated dispositional model and SSL-DXR PK was described by a two-compartment model with first-order drug release. A hybrid-physiologic, well-stirred model with partition coefficients (Kp) captured intratumor concentrations. Apoptotic responses driving tumor priming were modeled using nonlinear, time-dependent transduction functions. The tumor growth model used net first-order growth and death rate constants, and two transit compartments that captured the temporal displacement of tumor exposure versus effect, and apoptotic signals from each agent were used to drive cytotoxic effects of the combination. The final model captured plasma and intratumor PK data, apoptosis induction profiles, and tumor growth for all treatments/sequences. A feedback loop representing PAC-induced apoptosis effects on Kp(_DXR) enabled the model to capture tumor-priming effects. Simulations to explore time- and sequence-dependent effects of priming indicated that PAC priming increased K(p_DXR) 3-fold. The intratumor concentrations producing maximal and half-maximal effects were 18 and 7.2 μg/ml for PAC, and 17.6 and 14.3 μg/ml for SSL-DXR. The duration of drug-induced apoptosis was 27.4 h for PAC and 15.8 h for SSL-DXR. Simulations suggested that PAC administered 24 h before peak priming could increase efficacy 2.5-fold over experimentally reported results. The quantitative approach developed in this article is applicable for evaluating tumor-priming strategies using diverse agents.
authors.affiliation: Department of Pharmaceutical Sciences, 456 Kapoor Hall, University at Buffalo, State University of New York, Buffalo, NY 14214, USA. sihema@buffalo.edu
authors.forename: Sihem
authors.initials: S
authors.lastname: Ait-Oudhia
authors.forename: Robert M
authors.initials: RM
authors.lastname: Straubinger
authors.forename: Donald E
authors.initials: DE
authors.lastname: Mager
mesh_terms: D000465:Algorithms; D000818:Animals; D000972:Antineoplastic Agents, Phytogenic / Q000493:pharmacokinetics / Q000494:pharmacology*; D017209:Apoptosis / Q000187:drug effects; D045744:Cell Line, Tumor; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004337:Drug Carriers / Q000378:metabolism*; D016503:Drug Delivery Systems; D008081:Liposomes; D051379:Mice; D015233:Models, Statistical; D053758:Nanoparticles; D009369:Neoplasms / Q000378:metabolism; D017239:Paclitaxel / Q000493:pharmacokinetics / Q000494:pharmacology*; D011092:Polyethylene Glycols / Q000494:pharmacology
chemical_list: D000972:Antineoplastic Agents, Phytogenic; D004337:Drug Carriers; D008081:Liposomes; D011092:Polyethylene Glycols; D017239:Paclitaxel