The Temozolomide-Doxorubicin paradox in Glioblastoma in vitro-in silico preclinical drug-screening

Abstract

Adjuvant Temozolomide is considered the front-line Glioblastoma chemotherapeutic treatment; yet not all patients respond. Latest trends in clinical trials usually refer to Doxorubicin; yet it can lead to severe side-effects if administered in high doses. While Glioblastoma prognosis remains poor, little is known about the combination of the two chemotherapeutics. Patient-derived spheroids were generated and treated with a range of Temozolomide/Doxorubicin concentrations either as monotherapy or in combination. Optical microscopy was used to monitor the growth pattern and cell death. Based on the monotherapy experiments, we developed a probabilistic mathematical framework in order to describe the drug-induced effect at the single-cell level and simulate drug doses in combination assuming probabilistic independence. Doxorubicin was found to be effective in doses even four orders of magnitude less than Temozolomide in monotherapy. The combination therapy doses tested in vitro were able to lead to irreversible growth inhibition at doses where monotherapy resulted in relapse. In our simulations, we assumed both drugs are anti-mitotic; Temozolomide has a growth-arrest effect, while Doxorubicin is able to cumulatively cause necrosis. Interestingly, under no mechanistic synergy assumption, the in silico predictions underestimate the in vitro results. In silico models allow the exploration of a variety of potential underlying hypotheses. The simulated-biological discrepancy at certain doses indicates a supra-additive response when both drugs are combined. Our results suggest a Temozolomide-Doxorubicin dual chemotherapeutic scheme to both disable proliferation and increase cytotoxicity against Glioblastoma.

Keywords: Brain cancer; Computational models; Doxorubicin; Preclinical drug-screening; Temozolomide.

Figure 1

Clinicopathological results of the patient enrolled. (A) Lesion site of where the biopsy was taken from a 53-year-old male patient with GB in the temporal-occipital left hemisphere. Representative axial T1 multi-planar reformation (left) and coronal T1 (center), as well as T2 axial magnetic resonance images (right) are shown from the tumor central plane. Cells from the collected tissue sample were used to form the primary GBP08-P0 cell line. (B) GB biopsy. Hematoxylin and eosin (H&E) staining shows morphological characteristics. GFAP is a glial marker. Ki67 is a marker of proliferation. Original magnifications at 400x.

Figure 2

2D drug-response. Temozolomide (right) and Doxorubicin (left) growth inhibition plots for the U87MG (green) and the GBP08-P0 (red) cells, respectively. Equation (1) was used as described in Methods.

Figure 3

3D drug-response. (A) Reconstructed LSFM MIP images labeled for cell death (Draq7, shown in red). Representative GBP08-P0 primary spheroids are depicted for 3- and 7-days post-treatment (depicted as Day 7 and Day 11 post-seeding, respectively) for either control, monotherapy or combination therapy. Note the high imaging depths achieved inside the large control and TMZ-only treated spheroids. DOX autofluorescence (green) can be seen in the DOX-treated spheroids, where the concentration is higher than 0.9 μM in order to be detected. Spheroids of 1000 μM TMZ treatment are similar to 500 μM, and 0.1–0.3 μM DOX-treated spheroids are similar to control for the timepoints shown here. Scale bar is set to 100 microns. (B) Dose–response curves for TMZ (left), DOX (middle) and combination therapy (right). Dose–response curves refer only to GBP08-P0 spheroids. U87MG (green) and GBP08-P0 (red) untreated cells are used as a control between conditions. Notice that almost all combination therapy doses lead to irreversible growth inhibition.

Figure 4

Simulated drug-response. (A) Spatial distribution of cells for 3- and 7-days post-treatment for the control, TMZ, DOX and combination representative concentrations. Both the null hypothesis and the alternative fit simulations are shown for the combination therapy. Proliferative (blue), non-proliferative (green), dying (red), G0-arrested (yellow) cell states and debris (black) are depicted. (B) Monotherapy in silico–in vitro temporal evolution of growth inhibition (as opposed to the untreated condition) and cell death. Intrinsic cell death rate and lysis period are the same. There is long-term assumed probability for DOX-induced cell death. Drug uptake and diffusion are assumed as γtmz = 1.4 × 10⁻¹⁰ M/(cell s), Dtmz = 8.68e − 07cm²/s for TMZ and γdox = 1.4 × 10⁻¹³ M/(cell s), Ddox = 8.68e − 08cm²/s for DOX, respectively. (C) Combination therapy in silico–in vitro temporal evolution of growth inhibition (as opposed to the untreated condition) and cell death. For the null hypothesis, drug uptake and diffusion are assumed as in the respective monotherapy in B. For the alternative fit hypothesis, no diffusion coefficient and uptake are considered, while DOX is diluted by 2/3 every other day after 72 h.