Quantifying Drug Combination Synergy along Potency and Efficacy Axes

SOS1 Inhibition Enhances the Efficacy of KRASG12C Inhibitors and Delays Resistance in Lung Adenocarcinoma

The clinical effectiveness of KRASG12C inhibitors (G12Ci) is limited both by intrinsic and acquired resistance, necessitating the development of combination approaches. Here, we identified targeting proximal receptor tyrosine kinase signaling using the SOS1 inhibitor (SOS1i) BI-3406 as a strategy to improve responses to G12Ci treatment. SOS1i enhanced the efficacy of G12Ci and limited rebound receptor tyrosine kinase/ERK signaling to overcome intrinsic/adaptive resistance, but this effect was modulated by SOS2 protein levels. G12Ci drug-tolerant persister (DTP) cells showed up to a 3-fold enrichment of tumor-initiating cells (TIC), suggestive of a sanctuary population of G12Ci-resistant cells. SOS1i resensitized DTPs to G12Ci and inhibited G12C-induced TIC enrichment. Co-mutation of the tumor suppressor KEAP1 limited the clinical effectiveness of G12Ci, and KEAP1 and STK11 deletion increased TIC frequency and accelerated the development of acquired resistance to G12Ci, consistent with clinical G12Ci resistance seen with these co-mutations. Treatment with SOS1i both delayed acquired G12Ci resistance and limited the total number of resistant colonies regardless of KEAP1 and STK11 mutational status. Together, these data suggest that targeting SOS1 could be an effective strategy to both enhance G12Ci efficacy and prevent G12Ci resistance regardless of co-mutations. Significance: The SOS1 inhibitor BI-3406 both inhibits intrinsic/adaptive resistance and targets drug tolerant persister cells to limit the development of acquired resistance to clinical KRASG12C inhibitors in lung adenocarcinoma cells.

Resistance to spindle inhibitors in glioblastoma depends on STAT3 and therapy induced senescence

While mitotic spindle inhibitors specifically kill proliferating tumor cells without the toxicities of microtubule poisons, resistance has limited their clinical utility. Treating glioblastomas with the spindle inhibitors ispinesib, alisertib, or volasertib creates a subpopulation of therapy induced senescent cells that resist these drugs by relying upon the anti-apoptotic and metabolic effects of activated STAT3. Furthermore, these senescent cells expand the repertoire of cells resistant to these drugs by secreting an array of factors, including TGFβ, which induce proliferating cells to exit mitosis and become quiescent-a state that also resists spindle inhibitors. Targeting STAT3 restores sensitivity to each of these drugs by depleting the senescent subpopulation and inducing quiescent cells to enter the mitotic cycle. These results support a therapeutic strategy of targeting STAT3-dependent therapy-induced senescence to enhance the efficacy of spindle inhibitors for the treatment of glioblastoma.

Current and emerging strategies to curb antibiotic-resistant urinary tract infections

Rising rates of antibiotic resistance in uropathogenic bacteria compromise patient outcomes and prolong hospital stays. Consequently, new strategies are needed to prevent and control the spread of antibiotic resistance in uropathogenic bacteria. Over the past two decades, sizeable clinical efforts and research advances have changed urinary tract infection (UTI) treatment and prevention strategies to conserve antibiotic use. The emergence of antimicrobial stewardship, policies from national societies, and the development of new antimicrobials have shaped modern UTI practices. Future UTI management practices could be driven by the evolution of antimicrobial stewardship, improved and readily available diagnostics, and an improved understanding of how the microbiome affects UTI. Forthcoming UTI treatment and prevention strategies could employ novel bactericidal compounds, combinations of new and classic antimicrobials that enhance bacterial killing, medications that prevent bacterial attachment to uroepithelial cells, repurposing drugs, and vaccines to curtail the rising rates of antibiotic resistance in uropathogenic bacteria and improve outcomes in people with UTI.

EZH2 directly methylates PARP1 and regulates its activity in cancer

DNA repair dysregulation is a key driver of cancer development. Understanding the molecular mechanisms underlying DNA repair dysregulation in cancer cells is crucial for cancer development and therapies. Here, we report that enhancer of zeste homolog 2 (EZH2) directly methylates poly(adenosine diphosphate-ribose) polymerase-1 (PARP-1), an essential enzyme involved in DNA repair, and regulates its activity. Functionally, EZH2-catalyzed methylation represses PARP1 catalytic activity, down-regulates the recruitment of x-ray repair cross-complementing group-1 to DNA lesions and its associated DNA damage repair; on the other hand, it protects the cells from nicotinamide adenine dinucleotide overconsumption upon DNA damage formation. Meanwhile, EZH2-mediated methylation regulates PARP1 transcriptional and oncogenic activity, at least in part, through impairing PARP1-E2F1 interaction and E2F1 transcription factor activity. EZH2 and PARP1 inhibitors synergistically suppress prostate cancer growth. Collectively, our findings uncover an insight of EZH2 functions in fine-tuning PARP1 activity during DNA damage repair and cancer progression, which provides a rationale for combinational targeting EZH2 and PARP1 in cancer.

The ROCK-1/2 inhibitor RKI-1447 blocks N-MYC, promotes cell death, and emerges as a synergistic partner for BET inhibitors in neuroblastoma

High-risk neuroblastoma has a poor prognosis despite intensive treatment, highlighting the need for new therapeutic strategies. Genetic alterations in activators and inactivators of Rho GTPase have been identified in neuroblastoma suggested to activate Rho/Rho-kinase (ROCK) signaling. ROCK has also been implicated in therapy resistance. Therefore, we have explored the efficacy of the dual ROCK inhibitor RKI-1447 in neuroblastoma, emphasizing combination strategies. Treatment with RKI-1447 resulted in decreased growth, increased cell death, and inhibition of N-MYC in vitro and in vivo. A combination screen revealed enhanced effects between RKI-1447 and BET inhibitors. Synergistic effects from RKI-1447 and the BET inhibitor, ABBV-075, were confirmed in various neuroblastoma models, including zebrafish. Interestingly, ABBV-075 increased phosphorylation of both myosin light chain 2 and cofilin, downstream effectors of ROCK, increases that were blocked by adding RKI-1447. The combination treatment also augmented an inhibitory effect on C-MYC and, less pronounced, N-MYC protein expression. BET inhibitors have shown preclinical efficacy against neuroblastoma, but acquired resistance has limited their therapeutic benefit. We reveal that the combination of ROCK and BET inhibitors offers a promising treatment approach that can potentially mitigate resistance to BET inhibitors and reduce toxicity.

A first-in-class Wiskott-Aldrich syndrome protein activator with antitumor activity in hematologic cancers

Hematologic cancers are among the most common cancers in adults and children. Despite significant improvements in therapies, many patients still succumb to the disease. Therefore, novel therapies are needed. The Wiskott-Aldrich syndrome protein (WASp) family regulates actin assembly in conjunction with the Arp2/3 complex, a ubiquitous nucleation factor. WASp is expressed exclusively in hematopoietic cells and exists in two allosteric conformations: autoinhibited or activated. Here, we describe the development of EG-011, a first-in-class small molecule activator of the autoinhibited form of WASp. EG-011 possesses in vitro and in vivo antitumor activity as a single agent in lymphoma, leukemia, and multiple myeloma, including models of secondary resistance to PI3K, BTK, and proteasome inhibitors. The in vitro activity was confirmed in a lymphoma xenograft. Actin polymerization and WASp binding were demonstrated using multiple techniques. Transcriptome analysis highlighted homology with drugs inducing actin polymerization.

Drug-Drug Interactions and Synergy: From Pharmacological Models to Clinical Application

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. SIGNIFICANCE STATEMENT: Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug-drug interactions research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

A novel combination therapy for ER+ breast cancer suppresses drug resistance via an evolutionary double-bind

Chemotherapy remains a commonly used and important treatment option for metastatic breast cancer. A majority of ER+ metastatic breast cancer patients ultimately develop resistance to chemotherapy, resulting in disease progression. We hypothesized that an "evolutionary double-bind", where treatment with one drug improves the response to a different agent, would improve the effectiveness and durability of responses to chemotherapy. This approach exploits vulnerabilities in acquired resistance mechanisms. Evolutionary models can be used in refractory cancer to identify alternative treatment strategies that capitalize on acquired vulnerabilities and resistance traits for improved outcomes. To develop and test these models, ER+ breast cancer cell lineages sensitive and resistant to chemotherapy are grown in spheroids with varied initial population frequencies to measure cross-sensitivity and efficacy of chemotherapy and add-on treatments such as disulfiram combination treatment. Different treatment schedules then assessed the best strategy for reducing the selection of resistant populations. We developed and parameterized a game-theoretic mathematical model from this in vitro experimental data, and used it to predict the existence of a double-bind where selection for resistance to chemotherapy induces sensitivity to disulfiram. The model predicts a dose-dependent re-sensitization (a double-bind) to chemotherapy for monotherapy disulfiram.

Expanded profiling of WD repeat domain 5 inhibitors reveals actionable strategies for the treatment of hematologic malignancies

WD40 Repeat Domain 5 (WDR5) is a highly conserved nuclear protein that recruits MYC oncoprotein transcription factors to chromatin to stimulate ribosomal protein gene expression. WDR5 is tethered to chromatin via an arginine-binding cavity known as the "WIN" site. Multiple pharmacological inhibitors of the WDR5-interaction site of WDR5 (WINi) have been described, including those with picomolar affinity and oral bioavailability in mice. Thus far, however, WINi have only been shown to be effective against a number of rare cancer types retaining wild-type p53. To explore the full potential of WINi for cancer therapy, we systematically profiled WINi across a panel of cancer cells, alone and in combination with other agents. We report that WINi are unexpectedly active against cells derived from both solid and blood-borne cancers, including those with mutant p53. Among hematologic malignancies, we find that WINi are effective as a single agent against leukemia and diffuse large B cell lymphoma xenograft models, and can be combined with the approved drug venetoclax to suppress disseminated acute myeloid leukemia in vivo. These studies reveal actionable strategies for the application of WINi to treat blood-borne cancers and forecast expanded utility of WINi against other cancer types.

Ultrasensitive Response Explains the Benefit of Combination Chemotherapy Despite Drug Antagonism

Most aggressive lymphomas are treated with combination chemotherapy, commonly as multiple cycles of concurrent drug administration. Concurrent administration is in theory optimal when combination therapies have synergistic (more than additive) drug interactions. We investigated pharmacodynamic interactions in the standard 4-drug "CHOP" regimen in peripheral T-cell lymphoma (PTCL) cell lines and found that CHOP consistently exhibits antagonism and not synergy. We tested whether staggered treatment schedules could improve tumor cell kill by avoiding antagonism, using in vitro models of concurrent or staggered treatments. Surprisingly, we observed that tumor cell kill is maximized by concurrent drug administration despite antagonistic drug-drug interactions. We propose that an ultrasensitive dose response, as described in radiology by the linear-quadratic (LQ) model, can reconcile these seemingly contradictory experimental observations. The LQ model describes the relationship between cell survival and dose, and in radiology has identified scenarios favoring hypofractionated radiotherapy-the administration of fewer large doses rather than multiple smaller doses. Specifically, hypofractionated treatment can be favored when cells require an accumulation of DNA damage, rather than a "single hit," to die. By adapting the LQ model to combination chemotherapy and accounting for tumor heterogeneity, we find that tumor cell kill is maximized by concurrent administration of multiple drugs, even when chemotherapies have antagonistic interactions. Thus, our study identifies a new mechanism by which combination chemotherapy can be clinically beneficial that is not contingent on positive drug-drug interactions.

Solid lipid nanoparticle: A potent vehicle of the kaempferol for brain delivery through the blood-brain barrier in the focal cerebral ischemic rat

Kaempferol is major flavonoid present in Convolvulus pluricaulis. This phytochemical protects the brain against oxidative stress, neuro-inflammation, neurotoxicity, neurodegeneration and cerebral ischemia induced neuronal destruction. Kaempferol is poorly water soluble. Our study proved that solid lipid nanoparticles (SLNs) were efficient carrier of kaempferol through blood-brain barrier (BBB). Kaempferol was incorporated into SLNs prepared from stearic acid with polysorbate 80 by the process of ultrasonication. Mean particle size and zeta potential of kaempferol loaded solid lipid nanoparticles (K-SLNs) were 451.2 nm and -15.0 mV. Atomic force microscopy showed that K-SLNs were spherical in shape. Fourier transformed infrared microscopy (FTIR) showed that both stearic acid and kaempferol were present in K-SLNs. X-ray diffraction (XRD) and differential scanning calorimetry (DSC) revealed that the matrices of K-SLNs were in untidy crystalline state. Entraptment efficiency of K-SLNs was 84.92%. In-vitro drug release percentage was 93.24%. Kaempferol loaded solid lipid nanoparticles (K-SLNs) showed controlled release profile. In-vitro uptake study showed significant efficiency of K-SLNs to cross blood-brain barrier (BBB). After oral administration into the focal cerebral ischemic rat, accumulation of fluorescent labeled K-SLNs was observed in the brain cortex which confirmed its penetrability into the brain. It significantly decreased the neurological deficit, infarct volume and level of reactive oxygen species (ROS) and decreased the level of pro-inflammatory mediators like NF-κB and p-STAT3. Damaged neurons and brain texture were improved. This study indicated increased bioavailability of kaempferol into the brain tissue through SLNs formulation.

Purinergic Ca2+ Signaling as a Novel Mechanism of Drug Tolerance in BRAF-Mutant Melanoma

Drug tolerance is a major cause of relapse after cancer treatment. Despite intensive efforts, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels) and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma and points to actionable strategies to overcome it.

Resistance to Spindle Inhibitors in Glioblastoma Depends on STAT3 and Therapy Induced Senescence

While mitotic spindle inhibitors specifically kill proliferating tumor cells without the toxicities of microtubule poisons, resistance has limited their clinical utility. Treating glioblastomas with the spindle inhibitors ispinesib, alisertib, or volasertib creates a subpopulation of therapy induced senescent cells that resist these drugs by relying upon the anti-apoptotic and metabolic effects of activated STAT3. Furthermore, these senescent cells expand the repertoire of cells resistant to these drugs by secreting an array of factors, including TGFβ, which induce proliferating cells to exit mitosis and become quiescent-a state that also resists spindle inhibitors. Targeting STAT3 restores sensitivity to each of these drugs by depleting the senescent subpopulation and inducing quiescent cells to enter the mitotic cycle. These results support a therapeutic strategy of targeting STAT3-dependent therapy-induced senescence to enhance the efficacy of spindle inhibitors for the treatment of glioblastoma.

Highlights

• Resistance to non-microtubule spindle inhibitors limits their efficacy in glioblastoma and depends on STAT3.• Resistance goes hand in hand with development of therapy induced senescence (TIS).• Spindle inhibitor resistant glioblastomas consist of three cell subpopulations-proliferative, quiescent, and TIS-with proliferative cells sensitive and quiescent and TIS cells resistant.• TIS cells secrete TGFβ, which induces proliferative cells to become quiescent, thereby expanding the population of resistant cells in a spindle inhibitor resistant glioblastoma• Treatment with a STAT3 inhibitor kills TIS cells and restores sensitivity to spindle inhibitors.

Data-driven learning of structure augments quantitative prediction of biological responses

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement. Under different environmental perturbations, each biological system exhibits a unique, structured phenotypic response. This structure can be learned based on limited data and once learned, can constrain subsequent quantitative predictions. We demonstrate that SAR requires significantly fewer data comparing to other existing machine-learning methods to achieve a high prediction accuracy, first on simulated data, then on experimental data of various systems and input dimensions. We then show how a learned structure can guide effective design of new experiments. Our approach has implications for predictive control of biological systems and an integration of machine learning prediction and experimental design.

MT-125 Inhibits Non-Muscle Myosin IIA and IIB, Synergizes with Oncogenic Kinase Inhibitors, and Prolongs Survival in Glioblastoma

We have identified a NMIIA and IIB-specific small molecule inhibitor, MT-125, and have studied its effects in GBM. MT-125 has high brain penetrance and retention and an excellent safety profile; blocks GBM invasion and cytokinesis, consistent with the known roles of NMII; and prolongs survival as a single agent in murine GBM models. MT-125 increases signaling along both the PDGFR- and MAPK-driven pathways through a mechanism that involves the upregulation of reactive oxygen species, and it synergizes with FDA-approved PDGFR and mTOR inhibitors in vitro . Combining MT-125 with sunitinib, a PDGFR inhibitor, or paxalisib, a combined PI3 Kinase/mTOR inhibitor significantly improves survival in orthotopic GBM models over either drug alone, and in the case of sunitinib, markedly prolongs survival in ∼40% of mice. Our results provide a powerful rationale for developing NMII targeting strategies to treat cancer and demonstrate that MT-125 has strong clinical potential for the treatment of GBM.

Highlights

MT-125 is a highly specific small molecule inhibitor of non-muscle myosin IIA and IIB, is well-tolerated, and achieves therapeutic concentrations in the brain with systemic dosing.Treating preclinical models of glioblastoma with MT-125 produces durable improvements in survival.MT-125 stimulates PDGFR- and MAPK-driven signaling in glioblastoma and increases dependency on these pathways.Combining MT-125 with an FDA-approved PDGFR inhibitor in a mouse GBM model synergizes to improve median survival over either drug alone, and produces tumor free, prolonged survival in over 40% of mice.

Second-Order Effects of Chemotherapy Pharmacodynamics and Pharmacokinetics on Tumor Regression and Cachexia

Drug dose response curves are ubiquitous in cancer biology, but these curves are often used to measure differential response in first-order effects: the effectiveness of increasing the cumulative dose delivered. In contrast, second-order effects (the variance of drug dose) are often ignored. Knowledge of second-order effects may improve the design of chemotherapy scheduling protocols, leading to improvements in tumor response without changing the total dose delivered. By considering treatment schedules with identical cumulative dose delivered, we characterize differential treatment outcomes resulting from high variance schedules (e.g. high dose, low dose) and low variance schedules (constant dose). We extend a previous framework used to quantify second-order effects, known as antifragility theory, to investigate the role of drug pharmacokinetics. Using a simple one-compartment model, we find that high variance schedules are effective for a wide range of cumulative dose values. Next, using a mouse-parameterized two-compartment model of 5-fluorouracil, we show that schedule viability depends on initial tumor volume. Finally, we illustrate the trade-off between tumor response and lean mass preservation. Mathematical modeling indicates that high variance dose schedules provide a potential path forward in mitigating the risk of chemotherapy-associated cachexia by preserving lean mass without sacrificing tumor response.

Dual active pyrimidine-based carbocyclic nucleoside derivatives: synthesis, and in silico and in vitro anti-diabetic and anti-microbial studies

Diabetes mellitus (DM) is a chronic metabolic disorder marked by high blood glucose levels, impairing glucose production in the body. Its prevalence has steadily risen over the past decades, leading to compromised immunity and heightened susceptibility to microbial infections. Immune dysfunction associated with diabetes raises vulnerability, while neuropathy dulls sensation in the extremities, reducing injury awareness. Hence, the development of novel chemical compounds for anti-diabetic and anti-infective treatments is imperative to mitigate adverse effects. In this study, we designed and synthesized pyrimidine-based carbocyclic nucleoside derivatives with C-4 substitution to assess their potential in inhibiting α-glucosidase for managing diabetes mellitus (DM) and microbial infections. Compounds 8b and 10a displayed promising IC50 values against α-glucosidase (43.292 nmol and 48.638 nmol, respectively) and noteworthy docking energies (-9.4 kcal mol-1 and -10.3 kcal mol-1, respectively). Additionally, compounds 10a and 10b exhibited better antimicrobial activity against Bacillus cereus, with the zone of inhibition values of 2.2 ± 0.25 mm and 1.4 ± 0.1 mm at a 100 μl concentration, respectively. Compound 10a also exhibited a modest zone of inhibition of 1.2 ± 0.15 mm against Escherichia coli at 100 μl.

Modeling combination therapies in patient cohorts and cell cultures using correlated drug action

Characterizing the effect of combination therapies is vital for treating diseases like cancer. We introduce correlated drug action (CDA), a baseline model for the study of drug combinations in both cell cultures and patient populations, which assumes that the efficacy of drugs in a combination may be correlated. We apply temporal CDA (tCDA) to clinical trial data, and demonstrate the utility of this approach in identifying possible synergistic combinations and others that can be explained in terms of monotherapies. Using MCF7 cell line data, we assess combinations with dose CDA (dCDA), a model that generalizes other proposed models (e.g., Bliss response-additivity, the dose equivalence principle), and introduce Excess over CDA (EOCDA), a new metric for identifying possible synergistic combinations in cell culture.

Article review: Brazilin as potential anticancer agent

Brazilin is the main compound in Caesalpinia sappan and Haematoxylum braziletto, which is identified as a homoisoflavonoid based on its molecular structure. These plants are traditionally used as an anti-inflammatory to treat fever, hemorrhage, rheumatism, skin problems, diabetes, and cardiovascular diseases. Recently, brazilin has increased its interest in cancer studies. Several findings have shown that brazilin has cytotoxic effects on colorectal cancer, breast cancer, lung cancer, multiple myeloma, osteosarcoma, cervical cancer, bladder carcinoma, also other cancers, along with numerous facts about its possible mechanisms that will be discussed. Besides its flavonoid content, brazilin is able to chelate metal ions. A study has proved that brazilin could be used as an antituberculosis agent based on its ability to chelate iron. This possible iron-chelating of brazilin and all the studies discussed in this review will lead us to the statement that, in the future, brazilin has the potency to be a chemo-preventive and anticancer agent. The article review aimed to determine the brazilin mechanism and pathogenesis of cancer.

ERBB4-Mediated Signaling Is a Mediator of Resistance to PI3K and BTK Inhibitors in B-cell Lymphoid Neoplasms

BTK and PI3K inhibitors are among the drugs approved for the treatment of patients with lymphoid neoplasms. Although active, their ability to lead to long-lasting complete remission is rather limited, especially in the lymphoma setting. This indicates that tumor cells often develop resistance to the drugs. We started from a marginal zone lymphoma cell line, Karpas-1718, kept under prolonged exposure to the PI3Kδ inhibitor idelalisib until acquisition of resistance, or with no drug. Cells underwent transcriptome, miRNA and methylation profiling, whole-exome sequencing, and pharmacologic screening, which led to the identification of the overexpression of ERBB4 and its ligands HBEGF and NRG2 in the resistant cells. Cellular and genetic experiments demonstrated the involvement of this axis in blocking the antitumor activity of various BTK/PI3K inhibitors, currently used in the clinical setting. Addition of recombinant HBEGF induced resistance to BTK/PI3K inhibitors in parental cells and in additional lymphoma models. Combination with the ERBB inhibitor lapatinib was beneficial in resistant cells and in other lymphoma models already expressing the identified resistance factors. An epigenetic reprogramming sustained the expression of the resistance-related factors, and pretreatment with demethylating agents or EZH2 inhibitors overcame the resistance. Resistance factors were also shown to be expressed in clinical specimens. In conclusion, we showed that the overexpression of ERBB4 and its ligands represents a novel mechanism of resistance for lymphoma cells to bypass the antitumor activity of BTK and PI3K inhibitors and that targeted pharmacologic interventions can restore sensitivity to the small molecules.

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs' protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.

A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment

Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.

Synergy and antagonism between azacitidine and FLT3 inhibitors

Synergetic interactions between drugs can make a drug combination more effective. Alternatively, they may allow to use lower concentrations and thus avoid toxicities or side effects that not only cause discomfort but might also reduce the overall survival. Here, we studied whether synergy exists between agents that are used for treatment of acute myeloid leukaemia (AML). Azacitidine is a demethylation agent that is used in the treatment of AML patients that are unfit for aggressive chemotherapy. An activating mutation in the FLT3 gene is common in AML patients and in the absence of specific treatment makes prognosis worse. FLT3 inhibitors may be used in such cases. We sought to determine whether combination of azacitidine with a FLT3 inhibitor (gilteritinib, quizartinib, LT-850-166, FN-1501 or FF-10101) displayed synergy or antagonism. To this end, we calculated dose-response matrices of these drug combinations from experiments in human AML cells and subsequently analysed the data using a novel consensus scoring algorithm. The results show that combinations that involved non-covalent FLT3 inhibitors, including the two clinically approved drugs gilteritinib and quizartinib were antagonistic. On the other hand combinations with the covalent inhibitor FF-10101 had some range of concentrations where synergy was observed.

Targeted reversal of multidrug resistance in ovarian cancer cells using exosome‑encapsulated tetramethylpyrazine

The objective of the present study was to develop exosomes (EXOs) encapsulating tetramethylpyrazine (TMP) for the reversal of drug resistance in ovarian cancer therapy. Human A2780 cells were incubated with TMP for 48 h. Purified TMP‑primed EXOs (EXOs‑TMP) were isolated through ultracentrifugation. The developed EXOs‑TMP were characterized using techniques such as transmission electron microscopy, nanoparticle tracking analysis, Fluorescence microscopy and western blotting. Subsequently, MTT, western blotting and flow cytometry assays were performed to evaluate the biological effects in drug‑resistant A2780T cells. The results demonstrated that the incorporation of TMP into EXOs exhibited an anti‑ovarian cancer effect and markedly enhanced the antitumor efficacy of paclitaxel (PTX). Furthermore, it was identified that the ability of EXO‑TMP to reverse cell resistance was associated with the downregulation of multidrug resistance protein 1, multidrug resistant‑associated protein 1 and glutathione S‑transferase Pi protein expression. Flow cytometry analysis revealed that EXO‑TMP induced apoptosis in drug‑resistant cells and enhanced the apoptotic effect when combined with PTX. EXOs are naturally sourced, exhibit excellent biocompatibility and enable precise drug delivery to target sites, thereby reducing toxic side effects. Overall, EXO‑TMP exhibited direct targeting capabilities towards A2780T cells and effectively reduced their drug resistance. EXOs‑TMP provide a novel and effective drug delivery pathway for reversing drug resistance in ovarian cancer.

In situ modeling of acquired resistance to RTK/RAS-pathway-targeted therapies

Intrinsic and acquired resistance limit the window of effectiveness for oncogene-targeted cancer therapies. Here, we describe an in situ resistance assay (ISRA) that reliably models acquired resistance to RTK/RAS-pathway-targeted therapies across cell lines. Using osimertinib resistance in EGFR-mutated lung adenocarcinoma (LUAD) as a model system, we show that acquired osimertinib resistance can be significantly delayed by inhibition of proximal RTK signaling using SHP2 inhibitors. Isolated osimertinib-resistant populations required SHP2 inhibition to resensitize cells to osimertinib and reduce MAPK signaling to block the effects of enhanced activation of multiple parallel RTKs. We additionally modeled resistance to targeted therapies including the KRASG12C inhibitors adagrasib and sotorasib, the MEK inhibitor trametinib, and the farnesyl transferase inhibitor tipifarnib. These studies highlight the tractability of in situ resistance assays to model acquired resistance to targeted therapies and provide a framework for assessing the extent to which synergistic drug combinations can target acquired drug resistance.

ATR inhibition using gartisertib enhances cell death and synergises with temozolomide and radiation in patient-derived glioblastoma cell lines

Glioblastoma cells can restrict the DNA-damaging effects of temozolomide (TMZ) and radiation therapy (RT) using the DNA damage response (DDR) mechanism which activates cell cycle arrest and DNA repair pathways. Ataxia-telangiectasia and Rad3-Related protein (ATR) plays a pivotal role in the recognition of DNA damage induced by chemotherapy and radiation causing downstream DDR activation. Here, we investigated the activity of gartisertib, a potent ATR inhibitor, alone and in combination with TMZ and/or RT in 12 patient-derived glioblastoma cell lines. We showed that gartisertib alone potently reduced the cell viability of glioblastoma cell lines, where sensitivity was associated with the frequency of DDR mutations and higher expression of the G2 cell cycle pathway. ATR inhibition significantly enhanced cell death in combination with TMZ and RT and was shown to have higher synergy than TMZ+RT treatment. MGMT promoter unmethylated and TMZ+RT resistant glioblastoma cells were also more sensitive to gartisertib. Analysis of gene expression from gartisertib treated glioblastoma cells identified the upregulation of innate immune-related pathways. Overall, this study identifies ATR inhibition as a strategy to enhance the DNA-damaging ability of glioblastoma standard treatment, while providing preliminary evidence that ATR inhibition induces an innate immune gene signature that warrants further investigation.

Bacterial efflux pump modulators prevent bacterial growth in macrophages and under broth conditions that mimic the host environment

IMPORTANCE

Bacterial efflux pumps are critical for resistance to antibiotics and for virulence. We previously identified small molecules that inhibit efflux pumps (efflux pump modulators, EPMs) and prevent pathogen replication in host cells. Here, we used medicinal chemistry to increase the activity of the EPMs against pathogens in cells into the nanomolar range. We show by cryo-electron microscopy that these EPMs bind an efflux pump subunit. In broth culture, the EPMs increase the potency (activity), but not the efficacy (maximum effect), of antibiotics. We also found that bacterial exposure to the EPMs appear to enable the accumulation of a toxic metabolite that would otherwise be exported by efflux pumps. Thus, inhibitors of bacterial efflux pumps could interfere with infection not only by potentiating antibiotics, but also by allowing toxic waste products to accumulate within bacteria, providing an explanation for why efflux pumps are needed for virulence in the absence of antibiotics.

SOS1 inhibition enhances the efficacy of and delays resistance to G12C inhibitors in lung adenocarcinoma

Clinical effectiveness of KRAS G12C inhibitors (G12Cis) is limited both by intrinsic and acquired resistance, necessitating the development of combination approaches. We found that targeting proximal receptor tyrosine kinase (RTK) signaling using the SOS1 inhibitor (SOS1i) BI-3406 both enhanced the potency of and delayed resistance to G12Ci treatment, but the extent of SOS1i effectiveness was modulated by both SOS2 expression and the specific mutational landscape. SOS1i enhanced the efficacy of G12Ci and limited rebound RTK/ERK signaling to overcome intrinsic/adaptive resistance, but this effect was modulated by SOS2 protein levels. Survival of drug-tolerant persister (DTP) cells within the heterogeneous tumor population and/or acquired mutations that reactivate RTK/RAS signaling can lead to outgrowth of tumor initiating cells (TICs) that drive therapeutic resistance. G12Ci drug tolerant persister cells showed a 2-3-fold enrichment of TICs, suggesting that these could be a sanctuary population of G12Ci resistant cells. SOS1i re-sensitized DTPs to G12Ci and inhibited G12C-induced TIC enrichment. Co-mutation of the tumor suppressor KEAP1 limits the clinical effectiveness of G12Cis, and KEAP1 and STK11 deletion increased TIC frequency and accelerated the development of acquired resistance to G12Ci in situ. SOS1i both delayed acquired G12Ci resistance and limited the total number of resistant colonies regardless of KEAP1 and STK11 mutational status. These data suggest that SOS1i could be an effective strategy to both enhance G12Ci efficacy and prevent G12Ci resistance regardless of co-mutations.

Additivity predicts the efficacy of most approved combination therapies for advanced cancer

Most advanced cancers are treated with drug combinations. Rational design aims to identify synergistic combinations, but existing synergy metrics apply to preclinical, not clinical data. Here we propose a model of drug additivity for progression-free survival (PFS) to assess whether clinical efficacies of approved drug combinations are additive or synergistic. This model includes patient-to-patient variability in best single-drug response plus the weaker drug per patient. Among US Food and Drug Administration approvals of drug combinations for advanced cancers (1995-2020), 95% exhibited additive or less than additive effects on PFS times. Among positive or negative phase 3 trials published between 2014-2018, every combination that improved PFS was expected to succeed by additivity (100% sensitivity) and most failures were expected to fail (78% specificity). This study shows synergy is neither a necessary nor common property of clinically effective drug combinations. The predictable efficacy of approved combinations suggests that additivity can be a design principle for combination therapies.

Guiding model-driven combination dose selection using multi-objective synergy optimization

Despite the growing appreciation that the future of cancer treatment lies in combination therapies, finding the right drugs to combine and the optimal way to combine them remains a nontrivial task. Herein, we introduce the Multi-Objective Optimization of Combination Synergy - Dose Selection (MOOCS-DS) method for using drug synergy as a tool for guiding dose selection for a combination of preselected compounds. This method decouples synergy of potency (SoP) and synergy of efficacy (SoE) and identifies Pareto optimal solutions in a multi-objective synergy space. Using a toy combination therapy model, we explore properties of the MOOCS-DS algorithm, including how optimal dose selection can be influenced by the metric used to define SoP and SoE. We also demonstrate the potential of our approach to guide dose and schedule selection using a model fit to preclinical data of the combination of the PD-1 checkpoint inhibitor pembrolizumab and the anti-angiogenic drug bevacizumab on two lung cancer cell lines. The identification of optimally synergistic combination doses has the potential to inform preclinical experimental design and improve the success rates of combination therapies. Jel classificationDose Finding in Oncology.

Integrated chemical and genetic screens unveil FSP1 mechanisms of ferroptosis regulation

Ferroptosis, marked by iron-dependent lipid peroxidation, may present an Achilles heel for the treatment of cancers. Ferroptosis suppressor protein-1 (FSP1), as the second ferroptosis mainstay, efficiently prevents lipid peroxidation via NAD(P)H-dependent reduction of quinones. Because its molecular mechanisms have remained obscure, we studied numerous FSP1 mutations present in cancer or identified by untargeted random mutagenesis. This mutational analysis elucidates the FAD/NAD(P)H-binding site and proton-transfer function of FSP1, which emerged to be evolutionarily conserved among different NADH quinone reductases. Using random mutagenesis screens, we uncover the mechanism of action of next-generation FSP1 inhibitors. Our studies identify the binding pocket of the first FSP1 inhibitor, iFSP1, and introduce the first species-independent FSP1 inhibitor, targeting the NAD(P)H-binding pocket. Conclusively, our study provides new insights into the molecular functions of FSP1 and enables the rational design of FSP1 inhibitors targeting cancer cells.

A pleiotropic-epistatic entangelement model of drug response

Because drug response is multifactorial, graph models are uniquely powerful for comprehending its genetic architecture. We deconstruct drug response into many different and interdependent sub-traits, with each sub-trait controlled by multiple genes that act and interact in a complicated manner. The outcome of drug response is the consequence of multileveled intertwined interactions between pleiotropic effects and epistatic effects. Here, we propose a general statistical physics framework to chart the 3D geometric network that codes how epistasis pleiotropically influences a complete set of sub-traits to shape body-drug interactions. This model can dissect the topological architecture of epistatically induced pleiotropic networks (EiPN) and pleiotropically influenced epistatic networks (PiEN). We analyze and interpret the practical implications of the pleiotropic-epistatic entanglement model for pharmacogenomic studies.

Statistical Assessment of Drug Synergy from In Vivo Combination Studies Using Mouse Tumor Models

Drug combination therapy is a promising strategy for treating cancer; however, its efficacy and synergy require rigorous evaluation in preclinical studies before going to clinical trials. Existing methods have limited power to detect synergy in animal studies. Here, we introduce a novel approach to assess in vivo drug synergy with high sensitivity and low false discovery rate. It can accurately estimate combination index and synergy score under the Bliss independence model and the highest single agent (HSA) model without any assumption on tumor growth kinetics, study duration, data completeness and balance for tumor volume measurement. We show that our method can effectively validate in vitro drug synergy discovered from cell line assays in in vivo xenograft experiments, and can help to elucidate the mechanism of action for immune checkpoint inhibitors in syngeneic mouse models by combining an anti-PD-1 antibody and several tumor-infiltrating leukocytes depletion treatments. We provide a unified view of in vitro and in vivo synergy by presenting a parallelism between the fixed-dose in vitro and the 4-group in vivo combination studies, so they can be better designed, analyzed, and compared. We emphasize that combination index, when defined here via relative survival of tumor cells, is both dose and time dependent, and give guidelines on designing informative in vivo combination studies. We explain how to interpret and apply Bliss and HSA synergies. Finally, we provide an open-source software package named invivoSyn that enables automated analysis of in vivo synergy using our method and several other existing methods.

SIGNIFICANCE

This work presents a general solution to reliably determine in vivo drug synergy in single-dose 4-group animal combination studies.

Design, Synthesis, and Biological Evaluation of Novel Quercetin Derivatives as PPAR-γ Partial Agonists by Modulating Epithelial-Mesenchymal Transition in Lung Cancer Metastasis

Epithelial-to-mesenchymal transition (EMT) is responsible for driving metastasis of multiple cancer types including lung cancer. Peroxisome proliferator-activated receptor (PPAR)-γ, a ligand-activated transcription factor, controls expression of variety of genes involved in EMT. Although several synthetic compounds act as potent full agonists for PPAR-γ, their long term application is restricted due to serious adverse effects. Therefore, partial agonists involving reduced and balanced PPAR-γ activity are more effective and valued. A previous study discerned the efficacy of quercetin and its derivatives to attain favorable stabilization with PPAR-γ. Here this work is extended by synthesizing five novel quercetin derivatives (QDs) namely thiosemicarbazone (QUETSC)) and hydrazones (quercetin isonicotinic acid hydrazone (QUEINH), quercetin nicotinic acid hydrazone (QUENH), quercetin 2-furoic hydrazone (QUE2FH), and quercetin salicyl hydrazone (QUESH)) and their effects are analyzed in modulating EMT in lung cancer cell lines via PPAR-γ partial activation. QDs-treated A549 cells diminish cell proliferation strongly at nanomolar concentration compared to NCI-H460 cells. Of the five screened derivatives, QUETSC, QUE2FH, and QUESH exhibit the property of partial activation as compared to the overexpressive level of rosiglitazone. Consistently, these QDs also suppress EMT process by markedly downregulating the levels of mesenchymal markers (Snail, Slug, and zinc finger E-box binding homeobox 1) and concomitant upregulation of epithelial marker (E-cadherin).

Bacterial Efflux Pump Modulators Prevent Bacterial Growth in Macrophages and Under Broth Conditions that Mimic the Host Environment

New approaches for combatting microbial infections are needed. One strategy for disrupting pathogenesis involves developing compounds that interfere with bacterial virulence. A critical molecular determinant of virulence for Gram-negative bacteria are efflux pumps of the resistance-nodulation-division (RND) family, which includes AcrAB-TolC. We previously identified small molecules that bind AcrB, inhibit AcrAB-TolC, and do not appear to damage membranes. These efflux pump modulators (EPMs) were discovered in an in-cell screening platform called SAFIRE (Screen for Anti-infectives using Fluorescence microscopy of IntracellulaR Enterobacteriaceae). SAFIRE identifies compounds that disrupt the growth of a Gram-negative human pathogen, Salmonella enterica serotype Typhimurium (S. Typhimurium) in macrophages. We used medicinal chemistry to iteratively design ~200 EPM35 analogs and test them for activity in SAFIRE, generating compounds with nanomolar potency. Analogs were demonstrated to bind AcrB in a substrate binding pocket by cryo-electron microscopy (cryo-EM). Despite having amphipathic structures, the EPM analogs do not disrupt membrane voltage, as monitored by FtsZ localization to the cell septum. The EPM analogs had little effect on bacterial growth in standard Mueller Hinton Broth. However, under broth conditions that mimic the micro-environment of the macrophage phagosome, acrAB is required for growth, the EPM analogs are bacteriostatic, and increase the potency of antibiotics. These data suggest that under macrophage-like conditions the EPM analogs prevent the export of a toxic bacterial metabolite(s) through AcrAB-TolC. Thus, compounds that bind AcrB could disrupt infection by specifically interfering with the export of bacterial toxic metabolites, host defense factors, and/or antibiotics.

SynBa: improved estimation of drug combination synergies with uncertainty quantification

MOTIVATION

There exists a range of different quantification frameworks to estimate the synergistic effect of drug combinations. The diversity and disagreement in estimates make it challenging to determine which combinations from a large drug screening should be proceeded with. Furthermore, the lack of accurate uncertainty quantification for those estimates precludes the choice of optimal drug combinations based on the most favourable synergistic effect.

RESULTS

In this work, we propose SynBa, a flexible Bayesian approach to estimate the uncertainty of the synergistic efficacy and potency of drug combinations, so that actionable decisions can be derived from the model outputs. The actionability is enabled by incorporating the Hill equation into SynBa, so that the parameters representing the potency and the efficacy can be preserved. Existing knowledge may be conveniently inserted due to the flexibility of the prior, as shown by the empirical Beta prior defined for the normalized maximal inhibition. Through experiments on large combination screenings and comparison against benchmark methods, we show that SynBa provides improved accuracy of dose-response predictions and better-calibrated uncertainty estimation for the parameters and the predictions.

AVAILABILITY AND IMPLEMENTATION

The code for SynBa is available at https://github.com/HaotingZhang1/SynBa. The datasets are publicly available (DOI of DREAM: 10.7303/syn4231880; DOI of the NCI-ALMANAC subset: 10.5281/zenodo.4135059).

In situ modeling of acquired resistance to RTK/RAS pathway targeted therapies

Intrinsic and acquired resistance limit the window of effectiveness for oncogene-targeted cancer therapies. Preclinical studies that identify synergistic combinations enhance therapeutic efficacy to target intrinsic resistance, however, methods to study acquired resistance in cell culture are lacking. Here, we describe a novel in situ resistance assay (ISRA), performed in a 96-well culture format, that models acquired resistance to RTK/RAS pathway targeted therapies. Using osimertinib resistance in EGFR-mutated lung adenocarcinoma (LUAD) as a model system, we show acquired resistance can be reliably modeled across cell lines using objectively defined osimertinib doses. Similar to patient populations, isolated osimertinib-resistant populations showed resistance via enhanced activation of multiple parallel RTKs so that individual RTK inhibitors did not re-sensitize cells to osimertinib. In contrast, inhibition of proximal RTK signaling using the SHP2 inhibitor RMC-4550 both re-sensitized resistant populations to osimertinib and prevented the development of osimertinib resistance as a primary therapy. Similar, objectively defined drug doses were used to model resistance to additional RTK/RAS pathway targeted therapies including the KRASG12C inhibitors adagrasib and sotorasib, the MEK inhibitor trametinib, and the farnesyl transferase inhibitor tipifarnib. These studies highlight the tractability of in situ resistance assays to model acquired resistance to targeted therapies and provide a framework for assessing the extent to which synergistic drug combinations can target acquired drug resistance.

Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager

Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining popularity because they can improve patients' immunological responses by promoting T cell activation. The possibility of combining TCEs with checkpoint inhibitors to increase tumor response and patient survival has been highlighted by preclinical and clinical outcomes. However, identifying predictive biomarkers and optimal dose regimens for individual patients to benefit from combination therapy remains one of the main challenges. In this article, we describe a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that includes specific processes of immune-cancer cell interactions and was created based on published data on colorectal cancer. We generated a virtual patient cohort with the model to conduct in silico virtual clinical trials for combination therapy of a PD-L1 checkpoint inhibitor (atezolizumab) and a bispecific T cell engager (cibisatamab). Using the model calibrated against the clinical trials, we conducted several virtual clinical trials to compare various doses and schedules of administration for two drugs with the goal of therapy optimization. Moreover, we quantified the score of drug synergy for these two drugs to further study the role of the combination therapy.

The recent progress of deep-learning-based in silico prediction of drug combination

Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.

Neuropharmacological computational analysis of longitudinal electroencephalograms in clozapine-treated patients with schizophrenia using hierarchical dynamic causal modeling

The hierarchical characteristics of the brain are prominent in the pharmacological treatment of psychiatric diseases, primarily targeting cellular receptors that extend upward to intrinsic connectivity within a region, interregional connectivity, and, consequently, clinical observations such as an electroencephalogram (EEG). To understand the long-term effects of neuropharmacological intervention on neurobiological properties at different hierarchical levels, we explored long-term changes in neurobiological parameters of an N-methyl-D-aspartate canonical microcircuit model (CMM-NMDA) in the default mode network (DMN) and auditory hallucination network (AHN) using dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The neurobiological properties of the CMM-NMDA model associated with symptom improvement in schizophrenia were found across hierarchical levels, from a reduced membrane capacity of the deep pyramidal cell and intrinsic connectivity with the inhibitory population in DMN and intrinsic and extrinsic connectivity in AHN. The medication duration mainly affects the intrinsic connectivity and NMDA time constant in DMN. Virtual perturbation analysis specified the contribution of each parameter to the cross-spectral density (CSD) of the EEG, particularly intrinsic connectivity and membrane capacitances for CSD frequency shifts and progression. It further reveals that excitatory and inhibitory connectivity complements frequency-specific CSD changes, notably the alpha frequency band in DMN. Positive and negative synergistic interactions exist between neurobiological properties primarily within the same region in patients treated with clozapine. The current study shows how computational neuropharmacology helps explore the multiscale link between neurobiological properties and clinical observations and understand the long-term mechanism of neuropharmacological intervention reflected in clinical EEG.

Dose-response prediction for in-vitro drug combination datasets: a probabilistic approach

In this paper we propose PIICM, a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian process regression, to predict dose-response surfaces in untested drug combination experiments. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed. We show that the model can accurately predict dose-response in held out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs.

Statins synergize with phosphodiesterase type 5 inhibitors but not with selective estrogen receptor modulators to prevent myofibroblast transformation in an in vitro model of Peyronie's disease

BACKGROUND

Peyronie's disease (PD) is a fibrotic disorder characterized by plaque formation in the tunica albuginea (TA) of the penis, and we have previously shown that inhibition of transformation of TA-derived fibroblasts to myofibroblasts using a combination phosphodiesterase type 5 (PDE5) inhibitors and selective estrogen receptor modulators (SERMs) is effective in slowing the progression of early PD.

AIM

The study sought to investigate whether combinations of statins with PDE5 inhibitors or SERMs would affect myofibroblast transformation in vitro.

METHODS

Primary fibroblasts were isolated from TA of patients with PD and stimulated with transforming growth factor β1 in the absence and presence of a range of concentrations of statins, PDE5 inhibitors, SERMs, and their combinations for 72 hours before quantifying α-smooth muscle actin using in-cell enzyme-linked immunosorbent assay.

OUTCOMES

The prevention of transforming growth factor β1-induced transformation of TA-derived fibroblasts to myofibroblasts was measured in vitro.

RESULTS

Statins (simvastatin, lovastatin) inhibited myofibroblast transformation in a concentration-dependent manner with half maximal inhibitory concentration values of 0.77 ± 0.07 μM and 0.8 ± 0.13 μM, respectively. Simvastatin inhibited myofibroblast transformation in a synergistic fashion when combined with vardenafil (a PDE5 inhibitor; log alpha >0). Combination of tamoxifen (a SERM) and simvastatin did not show synergy (log alpha <0). When 3 drugs (simvastatin, vardenafil, and tamoxifen) were combined, the effect was not synergistic, but rather was additive.

CLINICAL IMPLICATIONS

A combination of a statin with a PDE5 inhibitor might be useful in the clinic to slow the progression of the disease in patients with early PD; however, caution should be taken with such a combination because of the reported myopathy as a side effect.

STRENGTHS AND LIMITATIONS

The use of primary human cells from patients with PD is a strength of this study. The mechanisms by which these drug classes exert synergy when used in combination was not investigated.

CONCLUSION

This is the first demonstration of an antifibrotic synergy between statins and PDE5 inhibitors.

Synergism of amlodipine and telmisartan or candesartan on blood pressure reduction by using SynergyFinder 3.0 and probability sum test in vivo

This study was designed to evaluate the synergism of two couples of antihypertensive drugs (amlodipine + telmisartan and amlodipine + candesartan) on blood pressure reduction in vivo by both SynergyFinder 3.0 and probability sum test. Spontaneously hypertensive rats were treated with intragastric administration of amlodipine (0.5, 1, 2, and 4 mg/kg), telmisartan (4, 8, and 16 mg/kg), candesartan (1, 2, and 4 mg/kg), nine combinations for amlodipine and telmisartan, and nine combinations for amlodipine and candesartan. The control rats were treated by 0.5% carboxymethylcellulose sodium. Blood pressure was recorded continuously up to 6 h after administration. Both SynergyFinder 3.0 and the probability sum test were used to evaluate the synergistic action. The synergisms calculated by SynergyFinder 3.0 are consistent with the probability sum test both in two different combinations. There is an obviously synergistic interaction between amlodipine and telmisartan or candesartan. The combinations of amlodipine and telmisartan (2 + 4 and 1 + 4 mg/kg) and amlodipine and candesartan (0.5 + 4 and 2 + 1 mg/kg) might exert an optimum synergism against hypertension. Compared with the probability sum test, SynergyFinder 3.0 is more stable and reliable to analyze the synergism.

Compound combinations targeting longevity: Challenges and perspectives

Aging is one of the world's greatest concerns, requiring urgent, effective, large-scale interventions to decrease the number of late-life chronic diseases and improve human healthspan. Anti-aging drug therapy is one of the most promising strategies to combat the effects of aging. However, most geroprotective compounds are known to successfully affect only a few aging-related targets. Given this, there is a great biological rationale for the use of combinations of anti-aging interventions. In this review, we characterize the various types of compound combinations used to modulate lifespan, discuss the existing evidence on their role in life extension, and present some key points about current challenges and future prospects for the development of combination drug anti-aging therapy.

Ultrasensitive response explains the benefit of combination chemotherapy despite drug antagonism

Most aggressive lymphomas are treated with combination chemotherapy, commonly as multiple cycles of concurrent drug administration. Concurrent administration is in theory optimal when combination therapies have synergistic (more than additive) drug interactions. We investigated pharmacodynamic interactions in the standard 4-drug 'CHOP' regimen in Peripheral T-Cell Lymphoma (PTCL) cell lines, and found that CHOP consistently exhibits antagonism and not synergy. We tested whether staggered treatment schedules could improve tumor cell kill by avoiding antagonism, using month-long in vitro models of concurrent or staggered treatments. Surprisingly, we observed that tumor cell kill is maximized by concurrent drug administration despite antagonistic drug-drug interactions. We propose that an ultrasensitive dose response, as described in radiology by the linear-quadratic (LQ) model, can reconcile these seemingly contradictory experimental observations. The LQ model describes the relationship between cell survival and dose, and in radiology has identified scenarios favoring hypofractionated radiation - the administration of fewer large doses rather than multiple smaller doses. Specifically, hypofractionated treatment can be favored when cells require an accumulation of DNA damage, rather than a 'single hit', in order to die. By adapting the LQ model to combination chemotherapy and accounting for tumor heterogeneity, we find that tumor cell kill is maximized by concurrent administration of multiple drugs, even when chemotherapies have antagonistic interactions. Thus, our study identifies a new mechanism by which combination chemotherapy can be clinically beneficial that is not reliant on positive drug-drug interactions.

Working with Convex Responses: Antifragility from Finance to Oncology

We extend techniques and learnings about the stochastic properties of nonlinear responses from finance to medicine, particularly oncology, where it can inform dosing and intervention. We define antifragility. We propose uses of risk analysis for medical problems, through the properties of nonlinear responses (convex or concave). We (1) link the convexity/concavity of the dose-response function to the statistical properties of the results; (2) define "antifragility" as a mathematical property for local beneficial convex responses and the generalization of "fragility" as its opposite, locally concave in the tails of the statistical distribution; (3) propose mathematically tractable relations between dosage, severity of conditions, and iatrogenics. In short, we propose a framework to integrate the necessary consequences of nonlinearities in evidence-based oncology and more general clinical risk management.

Hydrocinnamic Acid and Perillyl Alcohol Potentiate the Action of Antibiotics against Escherichia coli

The treatment of bacterial infections has been troubled by the increased resistance to antibiotics, instigating the search for new antimicrobial therapies. Phytochemicals have demonstrated broad-spectrum and effective antibacterial effects as well as antibiotic resistance-modifying activity. In this study, perillyl alcohol and hydrocinnamic acid were characterized for their antimicrobial action against Escherichia coli. Furthermore, dual and triple combinations of these molecules with the antibiotics chloramphenicol and amoxicillin were investigated for the first time. Perillyl alcohol had a minimum inhibitory concentration (MIC) of 256 µg/mL and a minimum bactericidal concentration (MBC) of 512 µg/mL. Hydrocinnamic acid had a MIC of 2048 µg/mL and an MBC > 2048 µg/mL. Checkerboard and time-kill assays demonstrated synergism or additive effects for the dual combinations chloramphenicol/perillyl alcohol, chloramphenicol/hydrocinnamic acid, and amoxicillin/hydrocinnamic acid at low concentrations of both molecules. Combenefit analysis showed synergism for various concentrations of amoxicillin with each phytochemical. Combinations of chloramphenicol with perillyl alcohol and hydrocinnamic acid revealed synergism mainly at low concentrations of antibiotics (up to 2 μg/mL of chloramphenicol with perillyl alcohol; 0.5 μg/mL of chloramphenicol with hydrocinnamic acid). The results highlight the potential of combinatorial therapies for microbial growth control, where phytochemicals can play an important role as potentiators or resistance-modifying agents.

Deoxyelephantopin, a germacrane-type sesquiterpene lactone from Elephantopus scaber, induces mitochondrial apoptosis of hepatocarcinoma cells by targeting Hsp90α in vitro and in vivo

Hepatocellular carcinoma has been known as the most frequent subtype of liver cancer with a high rate of spread, metastases, and recurrence, also dismal treatment effects. However, effective therapies for HCC are still required. Nowadays, natural products have been known as a valuable source for drug discovery. In this research, 44 sesquiterpene lactones isolated from the Elephantopus scaber Linn. (Asteraceae) were tested by MTT assay for the antitumor activities. Deoxyelephantopin (DET) was found to exert significant cytotoxicity on HepG2 and Hep3B cells. Moreover, we found that DET treatment markedly reduced the growth of HCC cells in a concentration-dependent manner, which was better than sorafenib. Furthermore, DET induced mitochondrial dysfunction, oxidative stress, and cellular apoptosis. Additionally, we found that DET and sorafenib synergistically induced apoptosis and mitochondrial dysfunction in HCC cells. DET combined with sorafenib was also efficacious in tumor xenograft model. Molecular docking experiments revealed that DET had a potentially high binding affinity with Hsp90α. Moreover, Drug Affinity Responsive Target Stability assay suggested that DET could directly target Hsp90α. Additionally, the expression of Hsp90α was both decreased in vitro and in vivo. Altogether, this study revealed that DET might be a promising agent for HCC therapy by targeting Hsp90α.

The Combination of Trametinib and Ganitumab is Effective in RAS-Mutated PAX-Fusion Negative Rhabdomyosarcoma Models

PURPOSE

PAX-fusion negative rhabdomyosarcoma (FN RMS) is driven by alterations in the RAS/MAP kinase pathway and is partially responsive to MEK inhibition. Overexpression of IGF1R and its ligands is also observed in FN RMS. Preclinical and clinical studies have suggested that IGF1R is itself an important target in FN RMS. Our previous studies revealed preclinical efficacy of the MEK1/2 inhibitor, trametinib, and an IGF1R inhibitor, BMS-754807, but this combination was not pursued clinically due to intolerability in preclinical murine models. Here, we sought to identify a combination of an MEK1/2 inhibitor and IGF1R inhibitor, which would be tolerated in murine models and effective in both cell line and patient-derived xenograft models of RAS-mutant FN RMS.

EXPERIMENTAL DESIGN

Using proliferation and apoptosis assays, we studied the factorial effects of trametinib and ganitumab (AMG 479), a mAb with specificity for human and murine IGF1R, in a panel of RAS-mutant FN RMS cell lines. The molecular mechanism of the observed synergy was determined using conventional and capillary immunoassays. The efficacy and tolerability of trametinib/ganitumab was assessed using a panel of RAS-mutated cell-line and patient-derived RMS xenograft models.

RESULTS

Treatment with trametinib and ganitumab resulted in synergistic cellular growth inhibition in all cell lines tested and inhibition of tumor growth in four of six models of RAS-mutant RMS. The combination had little effect on body weight and did not produce thrombocytopenia, neutropenia, or hyperinsulinemia in tumor-bearing SCID beige mice. Mechanistically, ganitumab treatment prevented the phosphorylation of AKT induced by MEK inhibition alone. Therapeutic response to the combination was observed in models without a mutation in the PI3K/PTEN axis.

CONCLUSIONS

We demonstrate that combined trametinib and ganitumab is effective in a genomically diverse panel of RAS-mutated FN RMS preclinical models. Our data also show that the trametinib/ganitumab combination likely has a favorable tolerability profile. These data support testing this combination in a phase I/II clinical trial for pediatric patients with relapsed or refractory RAS-mutated FN RMS.

Targeting IRAK4 with Emavusertib in Lymphoma Models with Secondary Resistance to PI3K and BTK Inhibitors

Inhibitors of phosphatidylinositol 3-kinase (PI3K) and Bruton tyrosine kinase (BTK) represent a recognized option for the treatment of patients affected by indolent B cell lymphomas. However, small molecules as single agents show limited success in their ability in inducing complete responses, with only partial remission achieved in most patients, suggesting the need for combination therapies. IRAK4 is a protein kinase downstream of the Toll-like receptor signaling (TLR), a driver pathway of secondary tumor° resistance in both hematological and solid tumor malignancies. Activation of IRAK4 upon TLRs and IL-1 receptor (IL-1R) stimulation and through the adaptor protein MYD88 initiates a signaling cascade that induces cytokine and survival factor expression mediated by the transcription factor NF-κB. MYD88-L265P encoding mutations occur in diffuse large B-cell lymphomas, in lymphoplasmacytic lymphomas and in few marginal zone lymphomas (MZL). The IRAK4 inhibitor emavusertib (CA-4948) has shown early safety and clinical activity in lymphoma and leukemia patients. In this preclinical study, we assessed emavusertib effectiveness in MZL, both as single agent and in combination with targeted agents, with a particular focus on its capability to overcome resistance to BTK and PI3K inhibitors. We showed that the presence of MYD88 L265P mutation in bona fide MZL cell lines confers sensitivity to the IRAK4 inhibitor emavusertib as single agent. Emavusertib-based combinations improved the sensitivity of MZL cells to BTK and PI3K inhibitors, including cells with a secondary resistance to these agents. Emavusertib exerted its activity via inhibition of NF-κB signaling and induction of apoptosis. Considering the early safety data from clinical trials, our study identifies the IRAK4 inhibitor emavusertib as a novel compound to be explored in trials for patients with MYD88-mutated indolent B cell lymphomas as single agent and as combination partner with BTK or PI3K inhibitors in unselected populations of patients.