Model Dinamika Terapi Kombinasi Chemotherapy dan Virotherapy pada Glioblastoma

Abstract

Glioblastoma bersifat resistan terhadap chemotherapy sehingga virotherapy menjadi alternatif. Efektivitas virotherapy bergantung pada ekspresi reseptor virus, seperti coxsackievirus and adenovirus receptor (CAR), yang dapat ditingkatkan dengan MEK inhibitor. Kombinasi chemotherapy, virotherapy, dan MEK inhibitor dilakukan untuk mengoptimalkan terapi. Model matematika berbasis persamaan diferensial biasa (PDB) dengan metode Euler digunakan untuk menyimulasikan dinamika terapi ini. Hasil simulasi menunjukkan terapi kombinasi lebih efektif daripada monotherapy dalam membunuh sel kanker. Penjadwalan terapi yang tepat dapat meningkatkan efektivitas pengobatan. Penambahan MEK inhibitor dalam tingkat penghambatan yang sesuai terbukti meningkatkan efektivitas terapi melalui peningkatan ekspresi CAR dan kontrol replikasi virus. Parameter yang paling berpengaruh dengan interaksi/nonlinearitas tinggi adalah laju infeksi sel kanker oleh virus, laju pertumbuhan sel kanker, viral burst size, dan laju lisis uninfected cancer cell oleh chemotherapy. Penelitian ini dapat dikembangkan dengan mempertimbangkan variabel biologis lain agar lebih sesuai dengan kondisi real.

Glioblastoma is resistant to chemotherapy, making virotherapy an alternative treatment option. The effectiveness of virotherapy depends on the expression of viral receptors, such as the coxsackievirus and adenovirus receptor (CAR), which can be enhanced using MEK inhibitors. A combination of chemotherapy, virotherapy, and MEK inhibitors is used to optimize treatment. A mathematical model based on ordinary differential equations (ODEs) using the Euler method was used to simulate the dynamics of this therapy. Simulation results showed that combination therapy is more effective than monotherapy in killing cancer cells. Proper scheduling of therapy can enhance treatment efficacy. The addition of MEK inhibitors at appropriate doses was found to enhance therapy efficacy through increased CAR expression and control of viral replication. The parameters most influenced by high interaction/nonlinearity are the rate of cancer cell infection by the virus, the rate of cancer cell growth, viral burst size, and the rate of lysis of uninfected cancer cells by chemotherapy. This research can be further developed by considering other biological variables to better align with real-world conditions.