- [ONCOLOGÍA RADIOTERÁPICA]
- [ONCOLOGÍA MÉDICA]
- [ANATOMÍA PATOLÓGICA]
- [CIRUGÍA GENERAL Y DIGESTIVA]
Neoadjuvant therapy for locally advanced gastric cancer patients. A population pharmacodynamic modeling
Patricia Martin-Romano (1), Belén P Solans (2), David Cano (3), Jose Carlos Subtil (4), Ana Chopitea (1), Leire Arbea (1), Maria Dolores Lozano (5), Eduardo Castanon (1), Iosune Baraibar (1), Diego Salas (1), Jose Luis Hernandez-Lizoain (6), Iñaki F Trocóniz (2), Javier Rodriguez (1)
(1) Department of Oncology, Clínica Universidad de Navarra, Pamplona, Spain.
(2) Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, Universidad de Navarra, Pamplona, Spain.
(3) Department of Radiology, Clinica Universidad de Navarra, Pamplona, Spain.
(4) Department of Gastroenterology, Clínica Universidad de Navarra, Pamplona, Spain.
(5) Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain.
(6) Department of Surgical Oncology, Clínica Universidad de Navarra, Pamplona, Spain.
Background: Perioperative chemotherapy (CT) or neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced gastric (GC) or gastroesophageal junction cancer (GEJC) has been shown to improve survival compared to an exclusive surgical approach. However, most patients retain a poor prognosis due to important relapse rates. Population pharmacokinetic-pharmacodynamic (PK/PD) modeling may allow identifying at risk-patients. We aimed to develop a mechanistic PK/PD model to characterize the relationship between the type of neoadjuvant therapy, histopathologic response and survival times in locally advanced GC and GEJC patients.
Methods: Patients with locally advanced GC and GEJC treated with neoadjuvant CT with or without preoperative CRT were analyzed. Clinical response was assessed by CT-scan and EUS. Pathologic response was defined as a reduction on pTNM stage compared to baseline cTNM. Metastasis development risk and overall survival (OS) were described using the population approach with NONMEM 7.3. Model evaluation was performed through predictive checks.
Results: A low correlation was observed between clinical and pathologic TNM stage for both T (R = 0.32) and N (R = 0.19) categories. A low correlation between clinical and pathologic response was noticed (R = -0.29). The OS model adequately described the observed survival rates. Disease recurrence, cTNM stage ≥3 and linitis plastica absence, were correlated to a higher risk of death.
Conclusion: Our model adequately described clinical response profiles, though pathologic response could not be predicted. Although the risk of disease recurrence and survival were linked, the identification of alternative approaches aimed to tailor therapeutic strategies to the individual patient risk warrants further research.
CITA DEL ARTÍCULO PLoS One. 2019 May 9;14(5):e0215970. doi: 10.1371/journal.pone.0215970. eCollection 2019