Randomized study of individualized pharmacokinetically‐guided dosing of paclitaxel compared with body‐surface area dosing in Chinese patients with advanced non‐small cell lung cancer

Abstract

This prospective, randomized study was initiated to assess the impact of pharmacokinetically (PK)-guided paclitaxel (PTX) dosing on toxicity and efficacy compared with body-surface area (BSA)-based dosing in Chinese non-small cell lung cancer patients.Methods: A total of 319 stage IIIB/IV non-small cell lung cancer patients receiving first-line chemotherapy were enrolled. Patients were randomized to receive 3-weekly carboplatin plus PTX at a starting dose of 175 mg/m 2 with subsequent PTX dosing based on either BSA or PK-guided dosing targeting time above a PTX plasma concentration of 0.05 μmol/L (PTX Tc > 0.05 ) between 26 and 31 hours. The primary safety endpoint was grade 4 haematological toxicity. The secondary endpoints were neuropathy, objective response rate, progression-free survival and overall survival.Results: In total, 275 (86%) patients completed ≥2 cycles of chemotherapy (140 in BSA arm and 135 in PK arm). In cycle 1, with the same PTX dose, average PTX Tc > 0.05 was 37 hours (range = 18-57 hours). Over cycles 2-4, patients in the PK arm had significantly lower average PTX doses and exposure compared with the BSA arm (128 vs 161 mg/m 2 , P < .0001 and 29 vs 35 hours, P < .0001). PK-guided dosing significantly reduced the cumulative incidence of grade 4 haematological toxicity (15% vs 24%, P = .004), grade 4 neutropenia (15% vs 23%, P = .009) and grade ≥ 2 neuropathy (8% vs 21%, P = .005). Objective response rate (32% vs 26%, P = .28) and overall survival (21.0 vs 24.0 months, P = .815) were similar in PK and BSA arms. Progressionfree survival was slightly improved in PK arm (4.67 vs 4.17 months, P = .026). Conclusion:PK-guided PTX dosing significantly reduced grade 4 haematological toxicities and grade ≥ 2 neuropathy without an adverse impact on clinical outcomes.

Time-to-Event Analysis of Paclitaxel-Associated Peripheral Neuropathy in Advanced Non–Small-Cell Lung Cancer Highlighting Key Influential Treatment/Patient Factors

“…Peripheral neuropathy (PN) is a major cumulative, often irreversible, dose-limiting toxicity of paclitaxel with significant impact on patient's quality of life and may influence treatment outcome (Stubblefield et al, 2009). Over twenty percent of patients receiving standard paclitaxel dosing (175 mg/m 2 -200 mg/m 2 , 3-weekly) against non-small cell lung cancer (NSCLC) experience clinically relevant PN (Joerger et al, 2016;Zhang et al, 2019). The currently accepted mechanism of paclitaxel-associated PN is through microtubule hyperstabilization, distorting the physiological cycle of microtubule depolymerization and repolymerization and subsequently interfering with axonal growth, intracellular transport and the structural integrity of neurons (Mielke et al, 2005;Gornstein and Schwarz, 2017).…”

Role of TDM‐based dose adjustments for taxane anticancer drugs

“…non‐TDM arm (4.67 vs . 4.17 months, P = 0.026) 29 . Overall, current data suggest individual paclitaxel T C>0.05 between 26 and 31 h to be adequate.…”

“…25 At least four prospective clinical studies explored paclitaxel therapeutic drug monitoring (TDM) and target concentration intervention (TCI) using Bayesian dose adjustments. [26][27][28][29] In the study by Woo and colleagues, seven children with refractory acute leukaemia were enrolled. 27 Furthermore, as shown in Figure 2…”

Artificial Intelligence and Machine Learning Applied at the Point of Care

“…A recent example highlighting the value of this approach to precision dosing is the RCT of paclitaxel-based chemotherapy in advanced non-small cell lung cancer comparing MIPD with traditional dosing based on body surface area (BSA). In this clinical study, which involved >300 patients, there were significantly lower rates of paclitaxel-induced toxicities (grade 4 neutropenia and > grade 2 neuropathy) in the MIPD arm compared to the BSA arm, without compromising efficacy (Zhang et al, 2019). In contrast to Bayesian forecasting, the use of AI/ML-derived algorithms as DSTs for precision dosing has received relatively little attention.…”