FEMALE PROMISCUITY IS POSITIVELY ASSOCIATED WITH NEUTRAL AND SELECTED GENETIC DIVERSITY IN PASSERINE BIRDS

Passerine birds show large interspecific variation in extrapair paternity rates. There is accumulating evidence that such promiscuous behavior is driven by indirect, genetic benefits to females. Sexual selection theory distinguishes between two types of genetic benefits, additive and nonadditive effects, mediated by preferences for good and compatible genes, respectively. Good genes preferences should imply directional selection and mating skew among males, and thus reduced genetic diversity in the population. In contrast, compatible genes preferences should give balancing selection that retains genetic diversity. Here, we test how well these predictions fit with patterns of variation in genetic diversity and promiscuity levels among passerine birds. We found that more promiscuous species had higher nucleotide diversity at autosomal introns, but not at Z‐chromosome introns. We also found that major histocompatibility complex (MHC) class IIB alleles had higher sequence diversity, and therefore should recognize a broader spectrum of pathogens, in more promiscuous species. Our results suggest that female promiscuity targets a multitude of autosomal genes for their nonadditive, compatibility benefits. Also, as immunity genes seem to be of particular importance, we hypothesize that interspecific variation in female promiscuity among passerine birds has arisen in response to the strength of pathogen‐mediated selection.     

Determining the Degree of Promiscuity of Extensively Assayed Compounds

In the context of polypharmacology, an emerging concept in drug discovery, promiscuity is rationalized as the ability of compounds to specifically interact with multiple targets. Promiscuity of drugs and bioactive compounds has thus far been analyzed computationally on the basis of activity annotations, without taking assay frequencies or inactivity records into account. Most recent estimates have indicated that bioactive compounds interact on average with only one to two targets, whereas drugs interact with six or more. In this study, we have further extended promiscuity analysis by identifying the most extensively assayed public domain compounds and systematically determining their promiscuity. These compounds were tested in hundreds of assays against hundreds of targets. In our analysis, assay promiscuity was distinguished from target promiscuity and separately analyzed for primary and confirmatory assays. Differences between the degree of assay and target promiscuity were surprisingly small and average and median degrees of target promiscuity of 2.6 to 3.4 and 2.0 were determined, respectively. Thus, target promiscuity remained at a low level even for most extensively tested active compounds. These findings provide further evidence that bioactive compounds are less promiscuous than drugs and have implications for pharmaceutical research. In addition to a possible explanation that drugs are more extensively tested for additional targets, the results would also support a “promiscuity enrichment model” according to which promiscuous compounds might be preferentially selected for therapeutic efficacy during clinical evaluation to ultimately become drugs.     

Drug Promiscuity in PDB: Protein Binding Site Similarity Is Key

Drug repositioning applies established drugs to new disease indications with increasing success. A pre-requisite for drug repurposing is drug promiscuity (polypharmacology) – a drug’s ability to bind to several targets. There is a long standing debate on the reasons for drug promiscuity. Based on large compound screens, hydrophobicity and molecular weight have been suggested as key reasons. However, the results are sometimes contradictory and leave space for further analysis. Protein structures offer a structural dimension to explain promiscuity: Can a drug bind multiple targets because the drug is flexible or because the targets are structurally similar or even share similar binding sites? We present a systematic study of drug promiscuity based on structural data of PDB target proteins with a set of 164 promiscuous drugs. We show that there is no correlation between the degree of promiscuity and ligand properties such as hydrophobicity or molecular weight but a weak correlation to conformational flexibility. However, we do find a correlation between promiscuity and structural similarity as well as binding site similarity of protein targets. In particular, 71% of the drugs have at least two targets with similar binding sites. In order to overcome issues in detection of remotely similar binding sites, we employed a score for binding site similarity: LigandRMSD measures the similarity of the aligned ligands and uncovers remote local similarities in proteins. It can be applied to arbitrary structural binding site alignments. Three representative examples, namely the anti-cancer drug methotrexate, the natural product quercetin and the anti-diabetic drug acarbose are discussed in detail. Our findings suggest that global structural and binding site similarity play a more important role to explain the observed drug promiscuity in the PDB than physicochemical drug properties like hydrophobicity or molecular weight. Additionally, we find ligand flexibility to have a minor influence.     

ASDCD: Antifungal Synergistic Drug Combination Database

Finding effective drugs to treat fungal infections has important clinical significance based on high mortality rates, especially in an immunodeficient population. Traditional antifungal drugs with single targets have been reported to cause serious side effects and drug resistance. Nowadays, however, drug combinations, particularly with respect to synergistic interaction, have attracted the attention of researchers. In fact, synergistic drug combinations could simultaneously affect multiple subpopulations, targets, and diseases. Therefore, a strategy that employs synergistic antifungal drug combinations could eliminate the limitations noted above and offer the opportunity to explore this emerging bioactive chemical space. However, it is first necessary to build a powerful database in order to facilitate the analysis of drug combinations. To address this gap in our knowledge, we have built the first Antifungal Synergistic Drug Combination Database (ASDCD), including previously published synergistic antifungal drug combinations, chemical structures, targets, target-related signaling pathways, indications, and other pertinent data. Its current version includes 210 antifungal synergistic drug combinations and 1225 drug-target interactions, involving 105 individual drugs from more than 12,000 references. ASDCD is freely available at http://ASDCD.amss.ac.cn.     

In silico drug combination discovery for personalized cancer therapy

Background

Drug combination therapy, which is considered as an alternative to single drug therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug combination therapies are widely used in the clinic for hypertension, asthma, and AIDS, they have also been proposed for the treatment of cancer. However, it is difficult to select and experimentally evaluate effective combinations because not only is the number of cancer drug combinations extremely large but also the effectiveness of drug combinations varies depending on the genetic variation of cancer patients. A computational approach that prioritizes the best drug combinations considering the genetic information of a cancer patient is necessary to reduce the search space.

Results

We propose an in-silico method for personalized drug combination therapy discovery. We predict the synergy between two drugs and a cell line using genomic information, targets of drugs, and pharmacological information. We calculate and predict the synergy scores of 583 drug combinations for 31 cancer cell lines. For feature dimension reduction, we select the mutations or expression levels of the genes in cancer-related pathways. We also used various machine learning models. Extremely Randomized Trees (ERT), a tree-based ensemble model, achieved the best performance in the synergy score prediction regression task. The correlation coefficient between the synergy scores predicted by ERT and the actual observations is 0.738. To compare with an existing drug combination synergy classification model, we reformulate the problem as a binary classification problem by thresholding the synergy scores. ERT achieved an F1 score of 0.954 when synergy scores of 20 and -20 were used as the threshold, which is 8.7% higher than that obtained by the state-of-the-art baseline model. Moreover, the model correctly predicts the most synergistic combination, from approximately 100 candidate drug combinations, as the top choice for 15 out of the 31 cell lines. For 28 out of the 31 cell lines, the model predicts the most synergistic combination in the top 10 of approximately 100 candidate drug combinations. Finally, we analyze the results, generate synergistic rules using the features, and validate the rules through the literature survey.

Conclusion

Using various types of genomic information of cancer cell lines, targets of drugs, and pharmacological information, a drug combination synergy prediction pipeline is proposed. The pipeline regresses the synergy level between two drugs and a cell line as well as classifies if there exists synergy or antagonism between them. Discovering new drug combinations by our pipeline may improve personalized cancer therapy.

     

Z LINKAGE OF FEMALE PROMISCUITY GENES IN THE MOTH UTETHEISA ORNATRIX: SUPPORT FOR THE SEXY‐SPERM HYPOTHESIS?

Female preference genes for large males in the highly promiscuous moth Utetheisa ornatrix (Lepidoptera: Arctiidae) have previously been shown to be mostly Z‐linked, in accordance with the hypothesis that ZZ–ZW sex chromosome systems should facilitate Fisherian sexual selection. We determined the heritability of both female and male promiscuity in the highly promiscuous moth U. ornatrix (Lepidoptera: Arctiidae) through parent–offspring and grandparent–offspring regression analyses. Our data show that male promiscuity is not sex‐limited and either autosomal or sex‐linked whereas female promiscuity is primarily determined by sex‐limited, Z‐linked genes. These data are consistent with the “sexy‐sperm hypothesis,” which posits that multiple‐mating and sperm competitiveness coevolve through a Fisherian‐like process in which female promiscuity is a kind of mate choice in which sperm‐competitiveness is the trait favored in males. Such a Fisherian process should also be more potent when female preferences are Z‐linked and sex‐limited than when autosomal or not limited.     

Pre-clinical drug prioritization via prognosis-guided genetic interaction networks

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call 'synergistic outcome determination' (SOD), a concept similar to 'Synthetic Lethality'. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.     

In vitro susceptibility of dermatomycoses agents to six antifungal drugs and evaluation by fractional inhibitory concentration index of combined effects of amorolfine and itraconazole in dermatophytes

To investigate the antifungal drug susceptibility of fungi responsible for dermatomycoses, minimum inhibition concentration (MIC) tests were performed in 44 strains of dermatophytes, including Trichophyton rubrum, Trichophyton mentagrophytes, Trichophyton verrucosum, Trichophyton tonsurans, Microsporum canis, Microsporum gypseum and Epidermophyton floccosum, with six antifungal drugs (amorolfine, terbinafine, butenafine, ketoconazole, itraconazole and bifonazole) by broth microdilution assay according to Clinical Laboratory Standard Institute protocols. Six possible dermatomycosis‐causing non‐dermatophytic fungi were also tested. The two major causes of tinea, T. rubrum and T. mentagrophytes, showed significantly different sensitivities to ketoconazole and bifonazole. Clinically derived dermatophytes were sensitive to the six antifungal drugs tested. However, non‐dermatophytes, especially Fusarium spp., tended to be resistant to these antifungal drugs. In Trichophyton spp., the MICs of non‐azole drugs had narrower distributions than those of azoles. To evaluate the effects of antifungal drug combinations, the fractional inhibitory concentration index was calculated for the combination of amorolfine and itraconazole as representative external and internal drugs for dermatophytes. It was found that this combination had synergistic or additive effects on most dermatophytes, and had no antagonistic effects. The variation in susceptibility of clinically derived fungal isolates indicates that identification of causative fungi is indispensable for appropriately choosing effective antifungal drugs in the early stages of infection. The results of combination assay suggest that multiple drugs with different antifungal mechanisms against growth of dermatophytes should be used to treat refractory dermatomycoses, especially onychomycosis.     

药物基因组学对癌症化疗的启示

药物基因组学的研究任务是阐明个体差异的遗传基础,并利用这些遗传信息来预测药物的疗效、毒性和安全性。绝大多数的癌症化疗药物在治疗效果及正常组织毒性上的个体差异一直广为关注。不仅诸多临床因素(如年龄、性别、饮食、药物相互作用等)与药物反应和治疗效果有关,而且药物分布(转运和代谢)和药物靶标的遗传变异同样可导致癌症治疗上的差异。本篇综述主要讨论当前和将来药物基因组学在临床癌症治疗和抗癌药物研制方面的应用。     

Fitness landscapes emerging from pharmacodynamic functions in the evolution of multidrug resistance

Adaptive evolution often involves beneficial mutations at more than one locus. In this case, the trajectory and rate of adaptation is determined by the underlying fitness landscape, that is, the fitness values and mutational connectivity of all genotypes under consideration. Drug resistance, especially resistance to multiple drugs simultaneously, is also often conferred by mutations at several loci so that the concept of fitness landscapes becomes important. However, fitness landscapes underlying drug resistance are not static but dependent on drug concentrations, which means they are influenced by the pharmacodynamics of the drugs administered. Here, I present a mathematical framework for fitness landscapes of multidrug resistance based on Hill functions describing how drug concentrations affect fitness. I demonstrate that these ‘pharmacodynamic fitness landscapes’ are characterized by pervasive epistasis that arises through (i) fitness costs of resistance (even when these costs are additive), (ii) nonspecificity of resistance mutations to drugs, in particular cross‐resistance, and (iii) drug interactions (both synergistic and antagonistic). In the latter case, reciprocal drug suppression may even lead to reciprocal sign epistasis, so that the doubly resistant genotype occupies a local fitness peak that may be difficult to access by evolution. Simulations exploring the evolutionary dynamics on some pharmacodynamic fitness landscapes with both constant and changing drug concentrations confirm the crucial role of epistasis in determining the rate of multidrug resistance evolution.     

Dopaminergic modulation of multi-muscle synergies in postural tasks performed by patients with Parkinson’s disease

BackgroundPostural instability is one of most disabling motor symptoms in Parkinson’s disease. Indices of multi-muscle synergies are new measurements of postural stability.ObjectivesWe explored the effects of dopamine-replacement drugs on multi-muscle synergies stabilizing center of pressure coordinate and their adjustments prior to a self-triggered perturbation in patients with Parkinson’s disease. We hypothesized that both synergy indices and synergy adjustments would be improved on dopaminergic drugs.MethodsPatients at Hoehn-Yahr stages II and III performed whole-body tasks both off- and on-drugs while standing. Muscle modes were identified as factors in the muscle activation space. Synergy indices stabilizing center of pressure in the anterior-posterior direction were quantified in the muscle mode space during a load-release task.ResultsDopamine-replacement drugs led to more consistent organization of muscles in stable groups (muscle modes). On-drugs patients showed larger indices of synergies and anticipatory synergy adjustments. In contrast, no medication effects were seen on anticipatory postural adjustments or other performance indices.ConclusionsDopamine-replacement drugs lead to significant changes in characteristics of multi-muscle synergies in Parkinson’s disease. Studies of synergies may provide a biomarker sensitive to problems with postural stability and agility and to efficacy of dopamine-replacement therapy.     

Selective Targeting of CTNNB1-, KRAS- or MYC-Driven Cell Growth by Combinations of Existing Drugs

The aim of combination drug treatment in cancer therapy is to improve response rate and to decrease the probability of the development of drug resistance. Preferably, drug combinations are synergistic rather than additive, and, ideally, drug combinations work synergistically only in cancer cells and not in non-malignant cells. We have developed a workflow to identify such targeted synergies, and applied this approach to selectively inhibit the proliferation of cell lines with mutations in genes that are difficult to modulate with small molecules. The approach is based on curve shift analysis, which we demonstrate is a more robust method of determining synergy than combination matrix screening with Bliss-scoring. We show that the MEK inhibitor trametinib is more synergistic in combination with the BRAF inhibitor dabrafenib than with vemurafenib, another BRAF inhibitor. In addition, we show that the combination of MEK and BRAF inhibitors is synergistic in BRAF-mutant melanoma cells, and additive or antagonistic in, respectively, BRAF-wild type melanoma cells and non-malignant fibroblasts. This combination exemplifies that synergistic action of drugs can depend on cancer genotype. Next, we used curve shift analysis to identify new drug combinations that specifically inhibit cancer cell proliferation driven by difficult-to-drug cancer genes. Combination studies were performed with compounds that as single agents showed preference for inhibition of cancer cells with mutations in either the CTNNB1 gene (coding for β-catenin), KRAS, or cancer cells expressing increased copy numbers of MYC. We demonstrate that the Wnt-pathway inhibitor ICG-001 and trametinib acted synergistically in Wnt-pathway-mutant cell lines. The ERBB2 inhibitor TAK-165 was synergistic with trametinib in KRAS-mutant cell lines. The EGFR/ERBB2 inhibitor neratinib acted synergistically with the spindle poison docetaxel and with the Aurora kinase inhibitor GSK-1070916 in cell lines with MYC amplification. Our approach can therefore efficiently discover novel drug combinations that selectively target cancer genes.     

Chemogenomics and orthology‐based design of antibiotic combination therapies

Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms.     

Interaction of meropenem with ‘N’ and ‘B’ isoforms of human serum albumin: a spectroscopic and molecular docking study

Carbapenems are used to control the outbreak of β-lactamases expressing bacteria. The effectiveness of drugs is influenced by its interaction with human serum albumin (HSA). Strong binding of carbapenems to HSA may lead to decreased bioavailability of the drug. The non-optimal drug dosage will provide a positive selection pressure on bacteria to develop resistance. Here, we investigated the interaction between meropenem and HSA at physiological pH 7.5 (N-isoform HSA) and non-physiological pH 9.2 (B-isoform HSA). Results showed that meropenem quenches the fluorescence of both ‘N’ and ‘B’ isoforms of HSA (ΔG < 0 and binding constant ~10⁴ M^?1). Electrostatic interactions and van der Waal interactions along with H-bonds stabilized the complex of meropenem with ‘N’ and ‘B’ isoforms of HSA, respectively. Molecular docking results revealed that meropenem binds to HSA near Sudlow’s site II (subdomain IIIA) close to Trp-214 with a contribution of a few residues of subdomain IIA. CD spectroscopy showed a change in the conformation of both the isoforms of HSA upon meropenem binding. The catalytic efficiency of HSA (only N-isoform) on p-nitrophenyl acetate was increased primarily due to a decrease in K_m and an increase in k_cat values. This study provides an insight into the molecular basis of interaction between meropenem and HSA.     

Cross‐species discovery of syncretic drug combinations that potentiate the antifungal fluconazole

Resistance to widely used fungistatic drugs, particularly to the ergosterol biosynthesis inhibitor fluconazole, threatens millions of immunocompromised patients susceptible to invasive fungal infections. The dense network structure of synthetic lethal genetic interactions in yeast suggests that combinatorial network inhibition may afford increased drug efficacy and specificity. We carried out systematic screens with a bioactive library enriched for off‐patent drugs to identify compounds that potentiate fluconazole action in pathogenic Candida and Cryptococcus strains and the model yeast Saccharomyces. Many compounds exhibited species‐ or genus‐specific synergism, and often improved fluconazole from fungistatic to fungicidal activity. Mode of action studies revealed two classes of synergistic compound, which either perturbed membrane permeability or inhibited sphingolipid biosynthesis. Synergistic drug interactions were rationalized by global genetic interaction networks and, notably, higher order drug combinations further potentiated the activity of fluconazole. Synergistic combinations were active against fluconazole‐resistant clinical isolates and an in vivo model of Cryptococcus infection. The systematic repurposing of approved drugs against a spectrum of pathogens thus identifies network vulnerabilities that may be exploited to increase the activity and repertoire of antifungal agents.     

Amine promiscuity and toxicology analysis

Drug discovery programs often face challenges to obtain sufficient duration of action of the drug (i.e. seek longer half-lives). If the pharmacodynamic response is driven by free plasma concentration of the drug then extending the plasma drug concentration is a valid approach. Half-life is dependent on the volume of distribution, which in turn can be dependent upon the ionization state of the molecule. Basic compounds tend to have a higher volume of distribution leading to longer half-lives. However, it has been shown that bases may also have higher promiscuity. In this work, we describe an analysis of in vitro pharmacological profiling and toxicology data investigating the role of primary, secondary, and tertiary amines in imparting promiscuity and thus off-target toxicity. Primary amines are found to be less promiscuous in in vitro assays and have improved profiles in in vivo toxicology studies compared to secondary and tertiary amines.     

Population differentiation and behavioural association of the two ‘personality’ genes DRD4 and SERT in dunnocks (Prunella modularis)

Quantifying the variation in behaviour‐related genes within and between populations provides insight into how evolutionary processes shape consistent behavioural traits (i.e. personality). Deliberate introductions of non‐native species offer opportunities to investigate how such genes differ between native and introduced populations and how polymorphisms in the genes are related to variation in behaviour. Here, we compared the genetic variation of the two ‘personality’ genes, DRD4 and SERT, between a native (United Kingdom, UK) and an introduced (New Zealand, NZ) population of dunnocks, Prunella modularis. The NZ population showed a significantly lower number of single nucleotide polymorphisms (SNPs) compared to the UK population. Standardized F’_st estimates of the personality genes and neutral microsatellites indicate that selection (anthropogenic and natural) probably occurred during and post the introduction event. Notably, the largest genetic differentiation was found in the intronic regions of the genes. In the NZ population, we also examined the association between polymorphisms in DRD4 and SERT and two highly repeatable behavioural traits: flight‐initiation distance and mating status (promiscuous females and cobreeding males). We found 38 significant associations (for different allele effect models) between the two behavioural traits and the studied genes. Further, 22 of the tested associations showed antagonistic allele effects for males and females. Our findings illustrate how introduction events and accompanying ecological changes could influence the genetic diversity of behaviour‐related genes.     

Substrate and metabolic promiscuities of d‐altronate dehydratase family proteins involved in non‐phosphorylative d‐arabinose,sugar acid,l‐galactose and l‐fucose pathways from bacteria

The gene context in microorganism genomes is of considerable help for identifying potential substrates. The C785_RS13685 gene in Herbaspirillum huttiense IAM 15032 is a member of the d‐ altronate dehydratase protein family, and which functions as a d‐ arabinonate dehydratase in vitro, is clustered with genes related to putative pentose metabolism. In the present study, further biochemical characterization and gene expression analyses revealed that l‐ xylonate is a physiological substrate that is ultimately converted to α‐ketoglutarate via so‐called Route II of a non‐phosphorylative pathway. Several hexonates, including d‐ altronate, d‐ idonate and l‐ gluconate, which are also substrates of C785_RS13685, also significantly up‐regulated the gene cluster containing C785_RS13685, suggesting a possibility that pyruvate and d‐ or l‐ glycerate were ultimately produced (novel Route III). On the contrary, ACAV_RS08155 of Acidovorax avenae ATCC 19860, a homologous gene to C785_RS13685, functioned as a d‐ altronate dehydratase in a novel l‐ galactose pathway, through which l‐ galactonate was epimerized at the C5 position by the sequential activity of two dehydrogenases, resulting in d‐ altronate. Furthermore, this pathway completely overlapped with Route III of the non‐phosphorylative l‐ fucose pathway. The ‘substrate promiscuity’ of d‐ altronate dehydratase protein(s) is significantly expanded to ‘metabolic promiscuity’ in the d‐ arabinose, sugar acid, l‐ fucose and l‐ galactose pathways.     

Landscape of Targeted Anti-Cancer Drug Synergies in Melanoma Identifies a Novel BRAF-VEGFR/PDGFR Combination Treatment

A newer generation of anti-cancer drugs targeting underlying somatic genetic driver events have resulted in high single-agent or single-pathway response rates in selected patients, but few patients achieve complete responses and a sizeable fraction of patients relapse within a year. Thus, there is a pressing need for identification of combinations of targeted agents which induce more complete responses and prevent disease progression. We describe the results of a combination screen of an unprecedented scale in mammalian cells performed using a collection of targeted, clinically tractable agents across a large panel of melanoma cell lines. We find that even the most synergistic drug pairs are effective only in a discrete number of cell lines, underlying a strong context dependency for synergy, with strong, widespread synergies often corresponding to non-specific or off-target drug effects such as multidrug resistance protein 1 (MDR1) transporter inhibition. We identified drugs sensitizing cell lines that are BRAF^V600E mutant but intrinsically resistant to BRAF inhibitor PLX4720, including the vascular endothelial growth factor receptor/kinase insert domain receptor (VEGFR/KDR) and platelet derived growth factor receptor (PDGFR) family inhibitor cediranib. The combination of cediranib and PLX4720 induced apoptosis in vitro and tumor regression in animal models. This synergistic interaction is likely due to engagement of multiple receptor tyrosine kinases (RTKs), demonstrating the potential of drug- rather than gene-specific combination discovery approaches. Patients with elevated biopsy KDR expression showed decreased progression free survival in trials of mitogen-activated protein kinase (MAPK) kinase pathway inhibitors. Thus, high-throughput unbiased screening of targeted drug combinations, with appropriate library selection and mechanistic follow-up, can yield clinically-actionable drug combinations.