A model-based clinically-relevant chemotherapy scheduling algorithm for anticancer agents

Chemotherapy is the most commonly employed method for systemic cancer treatment of\ud solid tumors and their metastases. The balance between cancer cell elimination and host\ud \ud toxicity minimization remains a challenge for clinicians when deploying chemotherapy treatments.\ud Our approach explicitly incorporates treatment-induced toxicities into the schedule\ud design. As a case study, we synthesize administration schedules for docetaxel, a widely\ud \ud used chemotherapeutic employed as a monoagent or in combination for the treatment of a\ud variety of cancers. The primary adverse effect of docetaxel treatment is myelosuppression,\ud characterized by neutropenia, a low plasma absolute neutrophil count (ANC). Through the\ud \ud use of model-based systems engineering tools, this thesis provides treatment schedules for\ud docetaxel and its combination therapies that reduce toxic side effects and improve patient\ud outcomes.\ud \ud The current algorithm employs models of tumor growth, drug pharmacokinetics, and\ud pharmacodynamics for both anticancer effects and toxicity, as characterized by ANC. Also\ud included is a toxicity-rescue therapy, with granulocyte colony stimulating factor (G-CSF)\ud that serves to elevate ANC. The single-agent docetaxel chemotherapy schedule minimizes\ud tumor volume over a multi-cycle horizon, subject to toxicity and logistical constraints imposed\ud by clinical practice.This single-agent chemotherapy scheduling formulation is extended to \ud combination chemotherapy using docetaxel-cisplatin or docetaxel-carboplatin drug pairs. \ud \ud \ud The two platinum agents display different toxicities, with cisplatin exhibiting kidney function \ud damage and carboplatin demonstrating the same myelosuppression effects as docetaxel. \ud These case studies provide two different challenges to the algorithm: (i) cisplatin scheduling significantly increases the\ud \ud number of variables and constraints, thereby challenging the computational engine and formulation;\ud (ii) carboplatin's overlapping toxicity tests the ability of the algorithm to schedule\ud drugs with different mechanisms of action (they act in different phases of the cellular growth\ud cycle) with the same toxic side effects. The simulated results demonstrate the algorithms \ud flexibility, in scheduling both docetaxel and cisplatin or carboplatin treatments for effective tumor\ud elimination and clinically acceptable toxicties. Overall, a clinically-relevant chemotherapy\ud scheduling optimization algorithm is provided for designing single agent and combination\ud chemotherapies, when toxicity and pharmacokinetic/pharmacodynamic information is available.\ud Furthermore, the algorithm can be extended to patient-specfic treatment by updating\ud the pharmacokinetic/pharmacodynamic models as data are collected during treatment.\u