A Real-world Toxicity Atlas Shows that Adverse Events of Combination Therapies Commonly Result in Additive Interactions.

PURPOSE: Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting. EXPERIMENTAL DESIGN: We provide here a proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid. Adverse event frequencies were shown as colors to assess the ratio between individual and combined drug effects. To capture these patterns, we trained a convolutional neural network (CNN) autoencoder using 7,300 single-drug heat maps. In addition, statistical synergy analyses were performed on the basis of BLISS independence or χ2 testing. RESULTS: The trained CNN model was able to decode patterns, showing that adverse events occur in global rather than isolated and unique patterns. Patterns were not likely to be attributed to disease symptoms given their relatively limited contribution to drug-associated adverse events. Pattern recognition was validated using trial data from ClinicalTrials.gov and drug combination data. We examined the adverse event interactions of 140 drug combinations known to be avoided in the clinic and found that near all of them showed additive rather than synergistic interactions, also when assessed statistically. CONCLUSIONS: Our study provides a framework for analyzing adverse events and suggests that adverse drug interactions commonly result in additive effects with a high level of overlap of adverse event patterns. These real-world insights may advance the implementation of new combination therapies in clinical practice.

Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures.

Background: In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays. Methods: We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15). Results: Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time. Conclusions: Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.

Screening of predicted synergistic multi-target therapies in glioblastoma identifies new treatment strategies.

Background: IDH-wildtype glioblastoma (GBM) is a highly malignant primary brain tumor with a median survival of 15 months after standard of care, which highlights the need for improved therapy. Personalized combination therapy has shown to be successful in many other tumor types and could be beneficial for GBM patients. Methods: We performed the largest drug combination screen to date in GBM, using a high-throughput effort where we selected 90 drug combinations for their activity onto 25 patient-derived GBM cultures. 43 drug combinations were selected for interaction analysis based on their monotherapy efficacy and were tested in a short-term (3 days) as well as long-term (18 days) assay. Synergy was assessed using dose-equivalence and multiplicative survival metrics. Results: We observed a consistent synergistic interaction for 15 out of 43 drug combinations on patient-derived GBM cultures. From these combinations, 11 out of 15 drug combinations showed a longitudinal synergistic effect on GBM cultures. The highest synergies were observed in the drug combinations Lapatinib with Thapsigargin and Lapatinib with Obatoclax Mesylate, both targeting epidermal growth factor receptor and affecting the apoptosis pathway. To further elaborate on the apoptosis cascade, we investigated other, more clinically relevant, apoptosis inducers and observed a strong synergistic effect while combining Venetoclax (BCL targeting) and AZD5991 (MCL1 targeting). Conclusions: Overall, we have identified via a high-throughput drug screening several new treatment strategies for GBM. Moreover, an exceptionally strong synergistic interaction was discovered between kinase targeting and apoptosis induction which is suitable for further clinical evaluation as multi-targeted combination therapy.

Data-driven prioritization and preclinical evaluation of therapeutic targets in glioblastoma.

BACKGROUND: Patients with glioblastoma (GBM) have a dismal prognosis, and there is an unmet need for new therapeutic options. This study aims to identify new therapeutic targets in GBM. METHODS: mRNA expression data of patient-derived GBM (n = 1279) and normal brain tissue (n = 46) samples were collected from Gene Expression Omnibus and The Cancer Genome Atlas. Functional genomic mRNA profiling was applied to capture the downstream effects of genomic alterations on gene expression levels. Next, a class comparison between GBM and normal brain tissue was performed. Significantly upregulated genes in GBM were further prioritized based on (1) known interactions with antineoplastic drugs, (2) current drug development status in humans, and (3) association with biologic pathways known to be involved in GBM. Antineoplastic agents against prioritized targets were validated in vitro and in vivo. RESULTS: We identified 712 significantly upregulated genes in GBM compared to normal brain tissue, of which 27 have a known interaction with antineoplastic agents. Seventeen of the 27 genes, including EGFR and VEGFA, have been clinically evaluated in GBM with limited efficacy. For the remaining 10 genes, RRM2, MAPK9 (JNK2, SAPK1a), and XIAP play a role in GBM development. We demonstrated for the MAPK9 inhibitor RGB-286638 a viability loss in multiple GBM cell culture models. Although no overall survival benefit was observed in vivo, there were indications that RGB-286638 may delay tumor growth. CONCLUSIONS: The MAPK9 inhibitor RGB-286638 showed promising in vitro results. Furthermore, in vivo target engagement studies and combination therapies with this compound warrant further exploration.

The TICking clock of EGFR therapy resistance in glioblastoma: Target Independence or target Compensation.

Targeted therapy against driver mutations responsible for cancer progression has been shown to be effective in many tumor types. For glioblastoma (GBM), the epidermal growth factor receptor (EGFR) gene is the most frequently mutated oncogenic driver and has therefore been considered an attractive target for therapy. However, so far responses to EGFR-pathway inhibitors have been disappointing. We performed an exhaustive analysis of the mechanisms that might account for therapy resistance against EGFR inhibition. We define two major mechanisms of resistance and propose modalities to overcome them. The first resistance mechanism concerns target independence. In this case, cells have lost expression of the EGFR protein and experience no negative impact of EGFR targeting. Loss of extrachromosomally encoded EGFR as present in double minute DNA is a frequent mechanism for this type of drug resistance. The second mechanism concerns target compensation. In this case, cells will counteract EGFR inhibition by activation of compensatory pathways that render them independent of EGFR signaling. Compensatory pathway candidates are platelet-derived growth factor ß (PDGFß), Insulin-like growth factor 1 (IGFR1) and cMET and their downstream targets, all not commonly mutated at the time of diagnosis alongside EGFR mutation. Given that both mechanisms make cells independent of EGFR expression, other means have to be found to eradicate drug resistant cells. To this end we suggest rational strategies which include the use of multi-target therapies that hit truncation mutations (mechanism 1) or multi-target therapies to co-inhibit compensatory proteins (mechanism 2).

Uremic solutes modulate hepatic bile acid handling and induce mitochondrial toxicity.

Chronic kidney disease (CKD) is accompanied by accumulating levels of uremic solutes in the circulation. Changes in the size and composition of the bile acid pool have also been observed. We investigated via which mechanisms uremic solutes may interfere with hepatocyte function and thus contribute to altered bile acid handling. We studied interference on the level of bile acid synthesis by cytochrome P450 7A1 (CYP7A1), explored effects on hepatic bile acid transporters, and investigated effects on mitochondrial function. In HEK293 cells overexpressing bile salt transporters, we observed that p-cresyl sulfate inhibited Na+-taurocholate cotransporting polypeptide (NTCP)-mediated uptake of taurocholic acid (TCA), whereas organic anion-transporting polypeptide 1B1 (OATP1B1)-mediated TCA uptake was increased. Assays in transporter-overexpressing membrane vesicles revealed that kynurenic acid inhibited TCA transport via the bile salt efflux pump (BSEP), whereas p-cresyl glucuronide and hippuric acid increased TCA efflux via multidrug resistance-associated protein 3 (MRP3). Moreover, indoxyl sulfate decreased mRNA expression of NTCP, OATP1B3 and CYP7A1 in primary human hepatocytes. Transport studies confirmed a decreased TCA uptake in indoxyl sulfate-exposed hepatocytes. Decreased hepatocyte viability was found for all seven uremic solutes tested, whereas five out of seven also decreased intracellular ATP levels and mitochondrial membrane potential. In conclusion, uremic solutes affect hepatic bile acid transport and mitochondrial function. This can contribute to the altered bile acid homeostasis observed in CKD patients.

WINDOW consortium: A path towards increased therapy efficacy against glioblastoma.

Glioblastoma is the most common and malignant form of brain cancer, for which the standard treatment is maximal surgical resection, radiotherapy and chemotherapy. Despite these interventions, mean overall survival remains less than 15 months, during which extensive tumor infiltration throughout the brain occurs. The resulting metastasized cells in the brain are characterized by chemotherapy resistance and extensive intratumoral heterogeneity. An orthogonal approach attacking both intracellular resistance mechanisms as well as intercellular heterogeneity is necessary to halt tumor progression. For this reason, we established the WINDOW Consortium (Window for Improvement for Newly Diagnosed patients by Overcoming disease Worsening), in which we are establishing a strategy for rational selection and development of effective therapies against glioblastoma. Here, we overview the many challenges posed in treating glioblastoma, including selection of drug combinations that prevent therapy resistance, the need for drugs that have improved blood brain barrier penetration and strategies to counter heterogeneous cell populations within patients. Together, this forms the backbone of our strategy to attack glioblastoma.