Biphasic Mathematical Model of Cell-Drug Interaction That Separates Target-Specific and Off-Target Inhibition and Suggests Potent Targeted Drug Combinations for Multi-Driver Colorectal Cancer Cells

In Vitro evaluation of the anti-pancreatic cancer activity of epimedium herb

Introduction: Pancreatic cancer (PC) is a particularly aggressive malignancy with limited therapeutic options. The search for innovative treatments has focused on traditional Chinese medicine, specifically epimedium. This research investigates epimedium's active ingredients, potential targets, and underlying mechanisms in treating PC. Methods: High-performance liquid chromatography (HPLC) was used to quantify the active components of epimedium and HPLC-Q-TOF-MS was employed for qualitative identification. Potential targets of epimedium's active ingredients were identified using the TCMSP, ETCM, CTD, and Swiss Target Prediction databases. Potential PC-related targets were sourced from DisGeNET, GeneCards, and OMIM databases. A Venn diagram was utilized to identify overlapping PC-related and epimedium targets. Core targets and pathways were elucidated through protein-protein interaction (PPI) network analysis, Gene Ontology (GO) assessments, and Reactome pathway enrichment analyses. Molecular docking techniques investigated interactions between active compounds and these targets. The expression and prognostic implications of target genes were evaluated using GEPIA2 and the Human Protein Atlas (HPA) databases. In vitro studies assessed the impact of epimedium extract (EPE) on Panc-1 cell viability, and Western blot analysis examined the expression levels of key targets. Results: Network pharmacological indicate that epimedium econtains active components such as baohuoside I, icariin, hyperoside, and epimedin B, which have potential therapeutic effects against PC. In vitro assays confirmed that EPE significantly reduced the viability of Panc-1 cells. Western blot analysis revealed a considerable decrease in the expression of key targets in EPE-treated cells, including AKT1, EGFR, p-EGFR, JUN, BCL2, IL6, and SRC. The R-HSA-1280215: Interleukin-4 and Interleukin-13 signaling pathways involving these genes were identified as potential therapeutic targets. Discussion: Epimedium holds promise as a candidate for treating PC. The modulation of interleukin-4 and interleukin-13 signaling pathways could be a pivotal mechanism by which epimedium impedes tumor development. Further research is warranted to validate these findings and explore the clinical applicability of epimedium in PC treatment.

Emergence of large-scale cell death through ferroptotic trigger waves

Large-scale cell death is commonly observed during organismal development and in human pathologies1-5. These cell death events extend over great distances to eliminate large populations of cells, raising the question of how cell death can be coordinated in space and time. One mechanism that enables long-range signal transmission is trigger waves6, but how this mechanism might be used for death events in cell populations remains unclear. Here we demonstrate that ferroptosis, an iron- and lipid-peroxidation-dependent form of cell death, can propagate across human cells over long distances (≥5 mm) at constant speeds (around 5.5 μm min-1) through trigger waves of reactive oxygen species (ROS). Chemical and genetic perturbations indicate a primary role of ROS feedback loops (Fenton reaction, NADPH oxidase signalling and glutathione synthesis) in controlling the progression of ferroptotic trigger waves. We show that introducing ferroptotic stress through suppression of cystine uptake activates these ROS feedback loops, converting cellular redox systems from being monostable to being bistable and thereby priming cell populations to become bistable media over which ROS propagate. Furthermore, we demonstrate that ferroptosis and its propagation accompany the massive, yet spatially restricted, cell death events during muscle remodelling of the embryonic avian limb, substantiating its use as a tissue-sculpting strategy during embryogenesis. Our findings highlight the role of ferroptosis in coordinating global cell death events, providing a paradigm for investigating large-scale cell death in embryonic development and human pathologies.

The Targeted Degradation of BRAF V600E Reveals the Mechanisms of Resistance to BRAF-Targeted Treatments in Colorectal Cancer Cells

The BRAF V600E mutation is frequently found in cancer. It activates the MAPK pathway and promotes cancer cell proliferation, making BRAF an excellent target for anti-cancer therapy. While BRAF-targeted therapy is highly effective for melanoma, it is often ineffective against other cancers harboring the BRAF mutation. In this study, we evaluate the effectiveness of a proteolysis targeting chimera (PROTAC), SJF-0628, in directing the degradation of mutated BRAF across a diverse panel of cancer cells and determine how these cells respond to the degradation. SJF-0628 treatment results in the degradation of BRAF V600E and a decrease in Mek activation in all cell lines tested, but the effects of the treatment on cell signaling and cell proliferation are cell-line-specific. First, BRAF degradation killed DU-4475 and Colo-205 cells via apoptosis but only partially inhibited the proliferation of other cancer cell lines. Second, SJF-0628 treatment resulted in co-degradation of MEK in Colo-205 cells but did not have the same effect in other cell lines. Finally, cell lines partially inhibited by BRAF degradation also contain other oncogenic drivers, making them multi-driver cancer cells. These results demonstrate the utility of a PROTAC to direct BRAF degradation and reveal that multi-driver oncogenesis renders some colorectal cancer cells resistant to BRAF-targeted treatment.

A unified approach to dissecting biphasic responses in cell signaling

Biphasic responses are encountered at all levels in biological systems. At the cellular level, biphasic dose-responses are widely encountered in cell signaling and post-translational modification systems and represent safeguards against overactivation or overexpression of species. In this paper, we provide a unified theoretical synthesis of biphasic responses in cell signaling systems, by assessing signaling systems ranging from basic biochemical building blocks to canonical network structures to well-characterized exemplars on one hand, and examining different types of doses on the other. By using analytical and computational approaches applied to a range of systems across levels (described by broadly employed models), we reveal (i) design principles enabling the presence of biphasic responses, including in almost all instances, an explicit characterization of the parameter space (ii) structural factors which preclude the possibility of biphasic responses (iii) different combinations of the presence or absence of enzyme-biphasic and substrate-biphasic responses, representing safeguards against overactivation and overexpression, respectively (iv) the possibility of broadly robust biphasic responses (v) the complete alteration of signaling behavior in a network due to biphasic interactions between species (biphasic regulation) (vi) the propensity of different co-existing biphasic responses in the Erk signaling network. These results both individually and in totality have a number of important consequences for systems and synthetic biology.

Challenges and Opportunities in Developing Targeted Therapies for Triple Negative Breast Cancer

Triple negative breast cancer (TNBC) is a heterogeneous group of breast cancers characterized by their lack of estrogen receptors, progesterone receptors, and the HER2 receptor. They are more aggressive than other breast cancer subtypes, with a higher mean tumor size, higher tumor grade, the worst five-year overall survival, and the highest rates of recurrence and metastasis. Developing targeted therapies for TNBC has been a major challenge due to its heterogeneity, and its treatment still largely relies on surgery, radiation therapy, and chemotherapy. In this review article, we review the efforts in developing targeted therapies for TNBC, discuss insights gained from these efforts, and highlight potential opportunities going forward. Accumulating evidence supports TNBCs as multi-driver cancers, in which multiple oncogenic drivers promote cell proliferation and survival. In such multi-driver cancers, targeted therapies would require drug combinations that simultaneously block multiple oncogenic drivers. A strategy designed to generate mechanism-based combination targeted therapies for TNBC is discussed.

Dasatinib in combination with BMS-754807 induce synergistic cytotoxicity in lung cancer cells through inhibiting lung cancer cell growth, and inducing autophagy as well as cell cycle arrest at the G1 phase

Lung cancer is the leading cause of cancer-related deaths worldwide. Combination of drugs targeting independent signaling pathways would effectively block the proliferation of cancer cells with lower concentrations and stronger synergy effects. Dasatinib, a multi-targeted protein tyrosine kinase inhibitor targeting BCR-ABL and kinases of SRC family, has been successfully applied in the treatment of chronic myeloid leukemia (CML). BMS-754807, an inhibitor targeting the insulin-like growth factor 1 receptor (IGF-IR) and insulin receptor (IR) family kinases, has been in phase I development for the treatment of a variety of human cancers. Herein, we demonstrated that dasatinib in combination with BMS-754807 inhibited lung cancer cell growth, while induced autophagy as well as cell cycle arrest at the G1 phase. Dasatinib in combination with BMS-754807 suppressed the expression of cell cycle marker proteins, Rb, p-Rb, CDK4, CDK6 and Cyclin D1, and the PI3K/Akt/mTOR signaling pathway. Dasatinib in combination with BMS-754807 induced autophagy in lung cancer cells, evidenced by the upregulation of LC3B II and beclin-1, the downregulation of LC3B I and SQSTM1/p62, and the autophagic flux observed with a confocal fluorescence microscopy. Furthermore, dasatinib (18 mg/kg) in combination with BMS-754807 (18 mg/kg) inhibited the growth of tumors in NCI-H3255 xenografts without changing the bodyweight. Overall, our results suggest that dasatinib in combination with BMS-754807 inhibits the lung cancer cell proliferation in vitro and tumor growth in vitro, which indicates promising evidence for the application of the drug combination in lung cancer therapy.

Isothiocyanate-Rich Extracts from Cauliflower (Brassica oleracea Var. Botrytis) and Radish (Raphanus sativus) Inhibited Metabolic Activity and Induced ROS in Selected Human HCT116 and HT-29 Colorectal Cancer Cells

Cruciferous vegetables such as cauliflower and radish contain isothiocyanates exhibiting chemoprotective effects in vitro and in vivo. This research aimed to assess the impact of cauliflower (CIE) and radish (RIE) isothiocyanate extracts on the metabolic activity, intracellular reactive oxygen species (ROS), and LDH production of selected human colorectal adenocarcinoma cells (HCT116 and HT-29 for early and late colon cancer development, respectively). Non-cancerous colon cells (CCD-33Co) were used as a cytotoxicity control. The CIE samples displayed the highest allyl isothiocyanate (AITC: 12.55 µg/g) contents, whereas RIE was the most abundant in benzyl isothiocyanate (BITC: 15.35 µg/g). Both extracts effectively inhibited HCT116 and HT-29 metabolic activity, but the CIE impact was higher than that of RIE on HCT116 (IC₅₀: 0.56 mg/mL). Assays using the half-inhibitory concentrations (IC₅₀) of all treatments, including AITC and BITC, displayed increased (p < 0.05) LDH (absorbance: 0.25-0.40 nm) and ROS release (1190-1697 relative fluorescence units) in both cell lines. BITC showed the highest in silico binding affinity with all the tested colorectal cancer molecular markers (NF-kB, β-catenin, and NRF2-NFE2). The theoretical evaluation of AITC and BITC bioavailability showed high values for both compounds. The results indicate that CIE and RIE extracts display chemopreventive effects in vitro, but additional experiments are needed to validate their effects.

Comparative kinase and cancer cell panel profiling of kinase inhibitors approved for clinical use from 2018 to 2020

During the last two decades, kinase inhibitors have become the major drug class for targeted cancer therapy. Although the number of approved kinase inhibitors increases rapidly, comprehensive in vitro profiling and comparison of inhibitor activities is often lacking in the public domain. Here we report the extensive profiling and comparison of 21 kinase inhibitors approved by the FDA for oncology indications since June 2018 and 13 previously approved comparators on panels of 255 biochemical kinase assays and 134 cancer cell line viability assays. Comparison of the cellular inhibition profiles of the EGFR inhibitors gefitinib, dacomitinib, and osimertinib identified the uncommon EGFR p.G719S mutation as a common response marker for EGFR inhibitors. Additionally, the FGFR inhibitors erdafitinib, infigratinib, and pemigatinib potently inhibited the viability of cell lines which harbored oncogenic alterations in FGFR1-3, irrespective of the specific clinical indications of the FGFR inhibitors. These results underscore the utility of in vitro kinase inhibitor profiling in cells for identifying new potential stratification markers for patient selection. Furthermore, comparison of the in vitro inhibition profiles of the RET inhibitors pralsetinib and selpercatinib revealed they had very similar biochemical and cellular selectivity. As an exception, an NTRK3 fusion-positive cell line was potently inhibited by pralsetinib but not by selpercatinib, which could be explained by the targeting of TRK kinases in biochemical assays by pralsetinib but not selpercatinib. This illustrates that unexpected differences in cellular activities between inhibitors that act through the same primary target can be explained by subtle differences in biochemical targeting. Lastly, FLT3-mutant cell lines were responsive to both FLT3 inhibitors gilteritinib and midostaurin, and the PI3K inhibitor duvelisib. Biochemical profiling revealed that the FLT3 and PI3K inhibitors targeted distinct kinases, indicating that unique dependencies can be identified by combined biochemical and cellular profiling of kinase inhibitors. This study provides the first large scale kinase assay or cell panel profiling study for newly approved kinase inhibitors, and shows that comprehensive in vitro profiling of kinase inhibitors can provide rationales for therapy selection and indication expansion of approved kinase inhibitors.

Growth‐rate model predicts in vivo tumor response from in vitro data

A major challenge in oncology drug development is to elucidate why drugs that show promising results in cancer cell lines in vitro fail in mouse studies or human trials. One of the fundamental steps toward solving this problem is to better predict how in vitro potency translates into in vivo efficacy. A common approach to infer whether a model will respond in vivo is based on in vitro half‐maximal inhibitory concentration values (IC₅₀), but yields limited quantitative comparison between cell lines and drugs, potentially because cell division and death rates differ between cell lines and in vivo models. Other methods based either on mechanistic modeling or machine learning require molecular insights or extensive training data, limiting their use for early drug development. To address these challenges, we propose a mathematical model integrating in vitro growth rate inhibition values with pharmacokinetic parameters to estimate in vivo drug response. Upon calibration with a drug‐specific factor, our model yields precise estimates of tumor growth rate inhibition for in vivo studies based on in vitro data. We then demonstrate how our model can be used to study dosing schedules and perform sensitivity analyses. In addition, it provides meaningful metrics to assess association with genotypes and guide clinical trial design. By relying on commonly collected data, our approach shows great promise for optimizing drug development, better characterizing the efficacy of novel molecules targeting proliferation, and identifying more robust biomarkers of sensitivity while limiting the number of in vivo experiments.

Identification of Lethal Inhibitors and Inhibitor Combinations for Mono-Driver versus Multi-Driver Triple-Negative Breast Cancer Cells

There are no signaling-based targeted therapies for triple-negative breast cancer. The development of targeted cancer therapy relies on identifying oncogenic signaling drivers, understanding their contributions to oncogenesis and developing inhibitors to block such drivers. In this study, we determine that DU-4475 is a mono-driver cancer cell line relying on BRAF and the mitogen-activated protein kinase pathway for viability and proliferation. It is fully and lethally inhibited by BRAF or Mek inhibitors at low nM concentrations, but it is resistant to inhibitors targeting other signaling pathways. The inhibitory lethality caused by blocking Mek or BRAF is through apoptosis. In contrast, MDA-MB-231 is a multi-driver triple-negative breast cancer cell line dependent on both Src and the KRAS-activated mitogen-activated kinase pathway for proliferation and viability. Blocking each pathway alone only partially inhibits cell proliferation without killing them, but the combination of dasatinib, an Src inhibitor, and trametinib, a Mek inhibitor, achieves synthetic lethality. The combination is highly potent, with an IC50 of 8.2 nM each, and strikingly synergistic, with a combination index of less than 0.003 for 70% inhibition. The synthetic lethality of the drug combination is achieved by apoptosis. These results reveal a crucial difference between mono-driver and multi-driver cancer cells and suggest that pharmacological synthetic lethality may provide a basis for effectively inhibiting multi-driver cancers.

Understanding the effect of measurement time on drug characterization

In order to determine correct dosage of chemotherapy drugs, the effect of the drug must be properly quantified. There are two important values that characterize the effect of the drug: εmax is the maximum possible effect of a drug, and IC50 is the drug concentration where the effect diminishes by half. There is currently a problem with the way these values are measured because they are time-dependent measurements. We use mathematical models to determine how the εmax and IC50 values depend on measurement time and model choice. Seven ordinary differential equation models (ODE) are used for the mathematical analysis; the exponential, Mendelsohn, logistic, linear, surface, Bertalanffy, and Gompertz models. We use the models to simulate tumor growth in the presence and absence of treatment with a known IC50 and εmax. Using traditional methods, we then calculate the IC50 and εmax values over fifty days to show the time-dependence of these values for all seven mathematical models. The general trend found is that the measured IC50 value decreases and the measured εmax increases with increasing measurement day for most mathematical models. Unfortunately, the measured values of IC50 and εmax rarely matched the values used to generate the data. Our results show that there is no optimal measurement time since models predict that IC50 estimates become more accurate at later measurement times while εmax is more accurate at early measurement times.

Application of a Biphasic Mathematical Model of Cancer Cell Drug Response for Formulating Potent and Synergistic Targeted Drug Combinations to Triple Negative Breast Cancer Cells

Triple negative breast cancer is a collection of heterogeneous breast cancers that are immunohistochemically negative for estrogen receptor, progesterone receptor, and ErbB2 (due to deletion or lack of amplification). No dominant proliferative driver has been identified for this type of cancer, and effective targeted therapy is lacking. In this study, we hypothesized that triple negative breast cancer cells are multi-driver cancer cells, and evaluated a biphasic mathematical model for identifying potent and synergistic drug combinations for multi-driver cancer cells. The responses of two triple negative breast cancer cell lines, MDA-MB-231 and MDA-MB-468, to a panel of targeted therapy drugs were determined over a broad range of concentrations. The analyses of the drug responses by the biphasic mathematical model revealed that both cell lines were indeed dependent on multiple drivers, and inhibitors of individual drivers caused a biphasic response: a target-specific partial inhibition at low nM concentrations, and an off-target toxicity at μM concentrations. We further demonstrated that combinations of drugs, targeting each driver, cause potent, synergistic, and cell-specific cell killing. Immunoblotting analysis of the effects of the individual drugs and drug combinations on the signaling pathways supports the above conclusion. These results support a multi-driver proliferation hypothesis for these triple negative breast cancer cells, and demonstrate the applicability of the biphasic mathematical model for identifying effective and synergistic targeted drug combinations for triple negative breast cancer cells.