Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer.
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ABSTRACT:
SUBMITTER: Weiss A
PROVIDER: S-EPMC4473022 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Antibody-drug conjugates (ADCs) have emerged as a promising class of anticancer agents. Currently, the Food and Drug Administration has granted approval to 12 compounds, with 2 later undergoing withdrawal. Moreover, several other compounds are currently under clinical development at different stages. Despite substantial antitumoral activity observed among different tumor types, adverse events and the development of resistance represent significant challenges in their use. Over the last years, an increasing number of clinical trials have been testing these drugs in different combinations with other anticancer agents, such as traditional chemotherapy, immune checkpoint inhibitors, monoclonal antibodies, and small targeted agents, reporting promising results based on possible synergistic effects and a potential for improved treatment outcomes among different tumor types. Here we will review combinations of ADCs with other antitumor agents aiming at describing the current state of the art and future directions.
| S-EPMC11222717 | biostudies-literature
Project description:The FITExP profiling approach was used to identify protein hits from cells incubated with RAPTA-T or RAPTA-EA. Validation experiments with paclitaxel and cisplatin underlined the reliability of the method and hit lists for both test compounds (and?) gave physiologically viable anti-cancer mechanisms. The hit proteins for paclitaxel match with those published and the ones obtained for cisplatin were related to DNA repair, which is in line with the principal mechanism of cisplatin cytotoxicity involving the formation of nuclear DNA lesions. RAPTA-T treatment leads to upregulation of multiple hits suggesting a broad mechanism of action involving both metastasis and tumorigenicity. In contrast, hit proteins identified after incubation with RAPTA-EA are linked to regulation of the oxidative stress response and are therefore thought to be conferred by the EA moiety in the drug. From a therapeutic standpoint, RAPTA-EA could be explored in cancers where EA alone has shown potency, such as chronic lymphocytic leukemias,91 or where EA combined with another agent shows synergy, such as the combination of EA with afatinib, an irreversible epidermal growth factor receptor tyrosine kinase inhibitors for breast cancers.92 On the other hand, due to its broad mechanism of action, RAPTA-T could potentially be more useful if used concomitantly with drugs that target specific cancer pathways and could also play a role in therapies for later stage cancers due to its anti-metastatic properties. Here, application of the FITExP allowed us to gauge the mechanistic pathways involved in the action of two novel ruthenium(II) metallodrugs with very different phenotypic characteristics and from this knowledge infer their role in therapy. Potentially, future application of the FITExP approach in this manner could be useful for identification of cancer chemotherapy drug combinations, where commonly cancer clinical trials fail at phase II where drug combinations are first tried and tested.
2018-02-09 | PXD005766 | Pride
Project description:The emerging technique termed functional identification of target by expression proteomics (FITExP) has been shown to identify the key protein targets of anti-cancer drugs. Here, we use this approach to elucidate the proteins involved in the mechanism of action of two ruthenium(II)-based anti-cancer compounds, RAPTA-T and RAPTA-EA in breast cancer cells, revealing significant differences in the proteins upregulated. RAPTA-T causes upregulation of multiple proteins suggesting a broad mechanism of action involving suppression of both metastasis and tumorigenicity. RAPTA-EA bearing a GST inhibiting ethacrynic acid moiety, causes upregulation of mainly oxidative stress related proteins. The approach used in this work could be applied to the prediction of effective drug combinations to test in cancer chemotherapy clinical trials.
| S-EPMC5431558 | biostudies-literature
Project description:Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms -- originally developed for digital communication -- modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs using only one-third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.
| S-EPMC2590660 | biostudies-literature
Project description:BackgroundEnzymes for plant cell wall deconstruction are a major cost in the production of ethanol from lignocellulosic biomass. The goal of this research was to develop optimized synthetic mixtures of enzymes for multiple pretreatment/substrate combinations using our high-throughput biomass digestion platform, GENPLAT, which combines robotic liquid handling, statistical experimental design and automated Glc and Xyl assays. Proportions of six core fungal enzymes (CBH1, CBH2, EG1, ?-glucosidase, a GH10 endo-?1,4-xylanase, and ?-xylosidase) were optimized at a fixed enzyme loading of 15 mg/g glucan for release of Glc and Xyl from all combinations of five biomass feedstocks (corn stover, switchgrass, Miscanthus, dried distillers' grains plus solubles [DDGS] and poplar) subjected to three alkaline pretreatments (AFEX, dilute base [0.25% NaOH] and alkaline peroxide [AP]). A 16-component mixture comprising the core set plus 10 accessory enzymes was optimized for three pretreatment/substrate combinations. Results were compared to the performance of two commercial enzymes (Accellerase 1000 and Spezyme CP) at the same protein loadings.ResultsWhen analyzed with GENPLAT, corn stover gave the highest yields of Glc with commercial enzymes and with the core set with all pretreatments, whereas corn stover, switchgrass and Miscanthus gave comparable Xyl yields. With commercial enzymes and with the core set, yields of Glc and Xyl were highest for grass stovers pretreated by AP compared to AFEX or dilute base. Corn stover, switchgrass and DDGS pretreated with AFEX and digested with the core set required a higher proportion of endo-?1,4-xylanase (EX3) and a lower proportion of endo-?1,4-glucanase (EG1) compared to the same materials pretreated with dilute base or AP. An optimized enzyme mixture containing 16 components (by addition of ?-glucuronidase, a GH11 endoxylanase [EX2], Cel5A, Cel61A, Cip1, Cip2, ?-mannanase, amyloglucosidase, ?-arabinosidase, and Cel12A to the core set) was determined for AFEX-pretreated corn stover, DDGS, and AP-pretreated corn stover. The optimized mixture for AP-corn stover contained more exo-?1,4-glucanase (i.e., the sum of CBH1 + CBH2) and less endo-?1,4-glucanase (EG1 + Cel5A) than the optimal mixture for AFEX-corn stover. Amyloglucosidase and ?-mannanase were the two most important enzymes for release of Glc from DDGS but were not required (i.e., 0% optimum) for corn stover subjected to AP or AFEX. As a function of enzyme loading over the range 0 to 30 mg/g glucan, Glc release from AP-corn stover reached a plateau of 60-70% Glc yield at a lower enzyme loading (5-10 mg/g glucan) than AFEX-corn stover. Accellerase 1000 was superior to Spezyme CP, the core set or the 16-component mixture for Glc yield at 12 h, but the 16-component set was as effective as the commercial enzyme mixtures at 48 h.ConclusionThe results in this paper demonstrate that GENPLAT can be used to rapidly produce enzyme cocktails for specific pretreatment/biomass combinations. Pretreatment conditions and feedstock source both influence the Glc and Xyl yields as well as optimal enzyme proportions. It is predicted that it will be possible to improve synthetic enzyme mixtures further by the addition of additional accessory enzymes.
| S-EPMC2964541 | biostudies-literature
Project description:Biomarkers associated with treatment-effect heterogeneity can be used to make treatment recommendations that optimize individual clinical outcomes. To accomplish this, statistical methods are needed to generate marker-based treatment-selection rules that can most effectively reduce the population burden due to disease and treatment. Compared to the standard approach of risk modeling to derive treatment-selection rules, a more robust approach is to directly minimize an unbiased estimate of total disease and treatment burden among a pre-specified class of rules. This problem is one of minimizing a weighted sum of 0-1 loss function, which is computationally challenging to solve due to the nonsmoothness of 0-1 loss. Huang and Fong, among others, proposed a method that uses the Ramp loss to approximate the 0-1 loss and solves the minimization problem through repetitive constrained optimizations. The algorithm was shown to have comparable or better performance than other comparative estimators in various settings. Our aim in this article is to further extend the algorithm to allow for variable selection in the presence of a large number of candidate markers.We develop an alternative method to derive marker combinations to minimize the weighted sum of Ramp loss in Huang and Fong, based on data from randomized trials. The new algorithm estimates treatment-selection rules by repetitively minimizing a smooth and differentiable objective function. Through the use of an L1 penalty, we expand the method to allow for feature selection and develop an algorithm based on the coordinate descent method to build the treatment-selection rule.Through extensive simulation studies, we compared performance of the proposed estimator to four existing approaches: (1) a logistic regression risk modeling approach, and three other "direct optimizing" approaches including (2) the estimator in Huang and Fong, (3) the weighted support vector machine, and (4) the weighted logistic regression. The proposed estimator performs comparably to that of Huang and Fong, and comparably or better than other estimators. Allowing for variable selection using the proposed estimator in the presence of a large number of markers further improves treatment-selection performance. The proposed estimator is also advantageous for selecting variables relevant to treatment selection compared to L1 penalized logistic regression and weighted logistic regression. We illustrate the application of the proposed methods in host-genetics data from an HIV vaccine trial.The proposed estimator is appealing considering its effectiveness and conceptual simplicity. It has significant potential to contribute to the selection and combination of biomarkers for treatment selection in clinical practice.
| S-EPMC4506270 | biostudies-literature
Project description:Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets.We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n?=?58) and after (n?=?60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n?=?18).We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment.To derive the greatest benefit from molecularly targeted drugs it is critical to design combination treatment strategies rationally. Incorporating knowledge of the tumor adaptation process into the design provides an opportunity to match targeted drugs to the evolving tumor phenotype and surmount resistance.
| S-EPMC4062052 | biostudies-literature
Project description:Treatment-selection markers predict an individual's response to different therapies, thus allowing for the selection of a therapy with the best predicted outcome. A good marker-based treatment-selection rule can significantly impact public health through the reduction of the disease burden in a cost-effective manner. Our goal in this article is to use data from randomized trials to identify optimal linear and nonlinear biomarker combinations for treatment selection that minimize the total burden to the population caused by either the targeted disease or its treatment. We frame this objective into a general problem of minimizing a weighted sum of 0-1 loss and propose a novel penalized minimization method that is based on the difference of convex functions algorithm (DCA). The corresponding estimator of marker combinations has a kernel property that allows flexible modeling of linear and nonlinear marker combinations. We compare the proposed methods with existing methods for optimizing treatment regimens such as the logistic regression model and the weighted support vector machine. Performances of different weight functions are also investigated. The application of the proposed method is illustrated using a real example from an HIV vaccine trial: we search for a combination of Fc receptor genes for recommending vaccination in preventing HIV infection.
| S-EPMC4277554 | biostudies-literature
Project description:Medical treatment for metastatic castration-resistant prostate cancer (mCRPC) patients has progressively been evolving from a nonspecific clinical approach to genomics-oriented therapies. The scientific community is in fact increasingly focusing on developing DNA damage repair (DDR) defect-driven novel molecules, both as single-agent therapy and in combined treatment strategies. Accordingly, research is under way into combined drug therapies targeting different pathways, e.g. androgen receptor signaling (ARS) and poly (adenosine diphosphate [ADP]-ribose) polymerase (PARP) enzymes, immune checkpoint (IC) and PARP, IC, and ARS, and prostate-specific membrane antigen (PSMA). In an attempt to formulate evolving treatment paradigms in mCRPC patients, here we selected clinical research into patients undergoing therapies with emerging molecules, with particular emphasis towards PARP-, IC-, and PSMA-inhibitors. In order to focus on those molecules and drug combinations most likely to be translated into routine clinical care in the near future, we selected only those clinical studies currently recruiting patients. A PubMed search focusing on the keywords "prostate cancer", "metastatic castration-resistant prostate cancer", "DDR pathways", "ARS inhibitors", "PARP inhibitors", "IC inhibitors", "PSMA-targeting agents", and "drug combinations" was performed.
| S-EPMC7349416 | biostudies-literature
Project description:Targeted therapeutics hold tremendous promise in inhibiting cancer cell proliferation. However, targeting proteins individually can be compensated for by bypass mechanisms and activation of regulatory loops. Designing optimal therapeutic combinations must therefore take into consideration the complex dynamic networks in the cell. In this study, we analyzed the insulin-like growth factor (IGF-1) signaling network in the MDA-MB231 breast cancer cell line. We used reverse-phase protein array to measure the transient changes in the phosphorylation of proteins after IGF-1 stimulation. We developed a computational procedure that integrated mass action modeling with particle swarm optimization to train the model against the experimental data and infer the unknown model parameters. The trained model was used to predict how targeting individual signaling proteins altered the rest of the network and identify drug combinations that minimally increased phosphorylation of other proteins elsewhere in the network. Experimental testing of the modeling predictions showed that optimal drug combinations inhibited cell signaling and proliferation, whereas nonoptimal combination of inhibitors increased phosphorylation of nontargeted proteins and rescued cells from cell death. The integrative approach described here is useful for generating experimental intervention strategies that could optimize drug combinations and discover novel pharmacologic targets for cancer therapy.
| S-EPMC2932856 | biostudies-literature