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  • 1
    Online Resource
    Online Resource
    Correction: Multidrug Resistant Pulmonary Tuberculosis Treatment Regimens and Patient Outcomes: An Individual Patient Data Meta-analysis of 9,153 Patients
    Public Library of Science (PLoS) ; 2012
    In:  PLoS Medicine Vol. 9, No. 9 ( 2012-9-14)
    Details
    In: PLoS Medicine, Public Library of Science (PLoS), Vol. 9, No. 9 ( 2012-9-14)
    Type of Medium: Online Resource
    ISSN: 1549-1676
    URL: Article
    DOI: 10.1371/annotation/230240bc-bcf3-46b2-9b21-2e6e584f7333
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2012
    detail.hit.zdb_id: 2164823-2
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  • 2
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    Abstract 5039: A data driven approach to predicting tissue-specific adverse events
    American Association for Cancer Research (AACR) ; 2017
    In:  Cancer Research Vol. 77, No. 13_Supplement ( 2017-07-01), p. 5039-5039
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 77, No. 13_Supplement ( 2017-07-01), p. 5039-5039
    Abstract: Adverse events are currently one of the main causes of failure in drug development and withdrawal after approval. As a result, predicting drug side effects is an incredibly important part of drug discovery and development. With the emergence of precision medicine there has been a surge in interest on creating drugs for specific protein targets, however we lack accurate ways to connect drug targets and mechanisms to specific side effects. Here we take a target-centric approach to in-silico drug side effect prediction. We have mined drug side effect databases and grouped sets of side effects to the originating human tissue. For each of 30+ tissues, we defined a set of “toxic targets”- proteins that are only targeted by drugs with toxicity in that tissue - and “safe targets” - proteins only targeted by drugs with no related tissue toxicities. We found that toxic targets are consistently more highly expressed than safe targets, indicating that their mechanisms may be more crucial in their respective tissue. Furthermore we found that toxic targets have higher network connectivity. Using published gene knockdown screens, we also found that toxic targets for each tissue are significantly more likely to be essential than safe targets and are more likely to be enriched for GO terms related to cell death. These pieces of information all reinforce the proposed relationship between the identified toxic targets and drug induced tissue toxicities. We next leveraged this information to draw insights into unexpected drug toxicity events. We applied the BANDIT drug target prediction tool to drugs misclassified by the PrOCTOR toxicity prediction method and drugs with a specific type of tissue toxicity that were not known to hit any of our identified toxic targets. We found that new drug-target predictions explained a large number of these toxicities, correctly classifying approximately five times as many side effects as would have been expected by random chance. These results all supported our target-centric hypothesis of drug side effect prediction. Therefore we built a set of machine-learning models that would integrate drug targets with tissue-wide expression patterns and gene-specific features to predict specific side effects for a given drug. We found that these methods could significantly outperform other prediction techniques and random chance. For instance, our method for predicting drug induced liver injury (DILI) had ~70% accuracy at pinpointing specific drugs known to cause DILI and its likelihood score correlated with the FDA’s reported DILI severity score. Overall these findings show how a target-centric approach to drug development can not only help us understand the relation between targets and specific phenotypic effects, but can help drug developers predict side-effects before costly and time-consuming clinical studies. Our hope is that adoption of these methods will lead to overall increase in drug development efficiency and bring safer drugs to the market quicker. Note: This abstract was not presented at the meeting. Citation Format: Kaitlyn M. Gayvert, Neel Madhukar, Coryandar Gilvary, Olivier Elemento. A data driven approach to predicting tissue-specific adverse events [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5039. doi:10.1158/1538-7445.AM2017-5039
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2017-5039
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 3
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    Abstract 5223: Differentiated receptor pharmacology of imipridone ONC201: The first DRD2 antagonist in clinical development for oncology
    American Association for Cancer Research (AACR) ; 2017
    In:  Cancer Research Vol. 77, No. 13_Supplement ( 2017-07-01), p. 5223-5223
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 77, No. 13_Supplement ( 2017-07-01), p. 5223-5223
    Abstract: ONC201 is the founding member of the imipridone class of compounds which selectively target G protein-coupled receptors (GPCRs). ONC201, currently in clinical trials, possesses an exceptional safety profile combined with anti-cancer effects that are driven by activation of the integrated stress response and inhibition of Ras signaling. In this study, we identified and characterized the previously unknown binding target of ONC201. Following its phenotypic discovery, a series of experiments indicated that ONC201 does not directly interact with many known cancer drug targets. BANDIT - a machine learning based drug target identification platform - predicted that ONC201 selectively antagonizes the GPCR dopamine receptor D2 (DRD2). DRD2 is overexpressed in many cancers, controls various pro-survival mechanisms including Ras signaling and stress pathways, and its antagonism causes anti-proliferative and pro-apoptotic effects in malignant cells. PathHunter® β-Arrestin and cAMP assays determined that ONC201 selectively antagonizes DRD2 and DRD3. Antipsychotics antagonize multiple dopamine receptor family members that belong to either the D1-like or D2-like subfamilies that cause opposing downstream effects. Consistent with BANDIT, in contrast to antipsychotics, ONC201 did not antagonize other dopamine receptors or other GPCRs with known ligands. Schild analysis and radioligand competition assays revealed a DRD2 affinity of ~3uM, consistent with ONC201 anticancer activity. In accordance with superior selectivity of ONC201 among the GPCR superfamily, ONC201 exhibited a wide therapeutic window in tumor versus normal cell viability assays compared to antipsychotics. In support of the hypothesis that selectively targeting D2-like receptors yields superior anti-cancer efficacy, combined DRD2/DRD1 inhibition with tool compounds was inferior to DRD2 inhibition alone. Further characterization revealed that ONC201 had a very slow association rate for DRD2 relative to antipsychotics, whereas the dissociation rate was similar to atypical antipsychotics that are well tolerated. Shotgun mutagenesis alanine scan mapping across 350 amino acids of DRD2 identified 8 residues critical for ONC201-mediated antagonism of dopamine-induced calcium flux. Several of these residues were not conserved among the dopamine receptor family and the residue with the largest effect is not conserved in any other family member, suggesting the basis of ONC201 specificity. Consistent with competitive inhibition, several residues were within the orthosteric binding site, however, two allosteric residues were also identified. In summary, ONC201 is the first DRD2 antagonist under clinical development for oncology and its differentiated receptor pharmacology explains its unique selectivity, anti-cancer activity, and safety that has been observed in clinical trials. Citation Format: Neel Madhukar, Varun Vijay Prabhu, Lakshmi Anantharaman, Chidananda Sulli, Edgar Davidson, Sean Deacon, Rohinton Tarapore, Joseph Rucker, Neil Charter, Banjamin Doranz, Wolfgang Oster, Olivier Elemento, Joshua Allen. Differentiated receptor pharmacology of imipridone ONC201: The first DRD2 antagonist in clinical development for oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5223. doi:10.1158/1538-7445.AM2017-5223
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2017-5223
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 4
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    Abstract 641: Systems biology identifies Gleevec as a specific inhibitor of CLIP-170S, a novel +TIP isoform, which causes taxane resistance in cancer cells and patients by obstructing the Microtubule pore
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Research Vol. 80, No. 16_Supplement ( 2020-08-15), p. 641-641
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 641-641
    Abstract: Taxanes are widely used in the treatment of solid tumor patients including gastric cancer (GC). Post-hoc analysis of the clinical trial that led to docetaxel approval in GC, revealed that patients with diffuse histological subtype were intrinsically resistant to taxanes. As yet, the molecular basis of clinical drug resistance remains poorly elucidated. Using a panel of GC cell lines, we identified a subset with intrinsic taxane resistance due to impaired drug-target engagement, in the absence of tubulin mutations or decreased drug accumulation. We discovered a novel, short variant of the microtubule (MT) +TIP binding protein CLIP-170, hereafter CLIP-170S, which was preferentially expressed in resistant cells. Mass-spec proteomics and 5'RACE showed that CLIP-170S lacked the first 150 amino acids, thus, missing the Cap-Gly domain required for +TIP localization. Microscopy of endogenous or exogenous proteins revealed that CLIP-170S was mislocalized from +TIP to the MT lattice in contrast to the canonical CLIP-170. Stable CLIP-170S knock down (KD) entirely reversed taxane-resistance (300 fold), directly establishing CLIP-170S as the cause of taxane resistance. Quantitation of Flutax-2 (fluorescently labeled taxane) binding kinetics by live-cell imaging of native cytoskeletons in sensitive and resistant cells, showed that Flutax-2 dissociated faster from MTs in CLIP-170S-expressing resistant cells due to slower association rate. CLIP-170S-KD fully restored Flutax-2 binding to MTs, indicating that CLIP-170S impedes taxane-MT interaction. As taxane binding to MT lumen requires entry via the MT pore, we used chemical probes binding at the outer-only (hexaflutax) or luminal (cyclostreptin) pore sites and showed reduced binding of both compounds to resistant cell cytoskeletons. In contrast, CLIP-170S had no effect on peluroside whose MT binding does not require access through the pore. Together, these data indicate that CLIP-170S obstructs the MT pore, preventing drug access to the MT lumen and causing taxane resistance. Clinically, we found CLIP-170S to be expressed in ~60% of GC patient tumors and that its expression was significantly associated with resistance to cabazitaxel monotherapy. Computational analyses of RNAseq data from sensitive and resistant cells predicted Gleevec (Imatinib) as a drug that could overcome taxane resistance. Indeed, we showed that Gleevec reversed taxane resistance by specific depletion of CLIP-170S protein. Taken together, these data reveal an entirely novel mechanism of taxane resistance via obstruction of the MT pore by the previously unrecognized CLIP-170S. We further found CLIP-170S to be highly prevalent in patient tumors and identified Gleevec as the first specific inhibitor of CLIP-170S. Citation Format: Prashant V. Thakkar, Katsuhiro Kita, Giuseppe Galletti, Neel Madhukar, Elena Vila Navarro, Kyle Cleveland, Isabel Barasoain, Holly V. Goodson, Dan Sackett, Jose Fernando Diaz, Olivier Elemento, Manish A. Shah, Paraskevi Giannakakou. Systems biology identifies Gleevec as a specific inhibitor of CLIP-170S, a novel +TIP isoform, which causes taxane resistance in cancer cells and patients by obstructing the Microtubule pore [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 641.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2020-641
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 5
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    Online Resource
    Abstract 5688: IND-enabling characterization of ONC206 as the next bitopic antagonist for oncology
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Research Vol. 80, No. 16_Supplement ( 2020-08-15), p. 5688-5688
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 5688-5688
    Abstract: ONC201 is the first clinical bitopic antagonist of dopamine receptor D2 (DRD2), that is well tolerated and induces durable tumor regressions in H3 K27M-mutant glioma patients. ONC206, a derivative of ONC201 that shares the imipridone core structure, is also a bitopic DRD2 antagonist that exhibits enhanced non-competitive effects, high specificity, nanomolar potency, and disruption of DRD2 homodimers. ONC206 exhibited a Ki of ~320nM for DRD2 with complete specificity across human GPCRs and complete DRD2 antagonism. Schild analyses of ONC206 in cAMP and β-Arrestin recruitment assays revealed hallmarks of non-competitive DRD2 antagonism, unlike antipsychotics but similar to ONC201. Shotgun mutagenesis across DRD2 identified 7 residues critical for ONC206-mediated antagonism at orthosteric and allosteric sites. Six residues were critical for ONC201 and ONC206, however the impact varied between the two compounds and one allosteric residue was exclusive to ONC206 located at the region that mediates the DRD2 homodimer interface. Gene expression profiling revealed ONC206 and ONC201 (upon 200nM treatment, 72 h) induce distinct signatures in U87 glioblastoma cells, further supporting distinct functional effects. Cell lines resistant to ONC201 and ONC206 are being generated to profile acquired-resistance mechanisms. Broad nanomolar efficacy of ONC206 (GI50 & lt;78-889nM, 72h) was observed in & gt;1,000 GDSC cancer cell lines with the highest sensitivity in cell lines exhibiting a DRD2+/DRD5- RNA expression signature. ONC206 reduced the viability of normal human fibroblasts at higher doses (GI50 & gt; 5µM), suggesting a wide therapeutic window. Antitumor efficacy without body weight loss was observed with 50 mg/kg weekly oral ONC206 in a dopamine-secreting HuCCT1 cholangiocarcinoma subcutaneous xenograft model. Oral ONC206 at 50mg/kg exhibited a ~12 µM plasma Cmax and ~6 hours terminal half-life in Sprague-Dawley rats. Additionally, 5-10 fold higher ONC206 concentrations were observed in adrenal gland, bile duct, brain and bone marrow relative to plasma. Nanomolar concentrations were also observed in the CSF above DRD2 antagonism thresholds, unlike ONC201. GLP toxicology studies with weekly oral ONC206 in Sprague-Dawley rats and beagle dogs revealed no dose-limiting toxicities. Mild and reversible body weight changes were observed at the highest evaluated dose in both species. The no observed adverse effect level was ≥ 16.7 mg/kg in dogs and ≥ 50 mg/kg in rats that exceed efficacious doses. A 50 mg starting dose of ONC206 was selected for the first-in-human clinical trial in biomarker-enriched adult recurrent CNS tumors. In summary, ONC206 is poised for clinical introduction as the next imipridone bitopic DRD2 antagonist for oncology that exhibits differentiated target engagement, signaling, and biodistribution profiles. Citation Format: Varun Vijay Prabhu, Sara Morrow, Caroline A. Cuoco, Abed R. Kawakibi, Jinkyu Jung, Neel Madhukar, Matthew J. Garnett, Ultan McDermott, Cyril H. Benes, Robert Wechsler-Reya, Lakshmi Anantharaman, Neil Charter, Joseph B. Rucker, Benjamin J. Doranz, Joel Basken, Olivier Elemento, R. Benjamin Free, David R. Sibley, Martin Stogniew, Wolfgang Oster, Mark R. Gilbert, Sharon DeMorrow. IND-enabling characterization of ONC206 as the next bitopic antagonist for oncology [abstract] . In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5688.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2020-5688
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 6
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    Online Resource
    Abstract 5063: Predicting target driven toxicity for small molecule inhibitors to aid in drug development
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 5063-5063
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 5063-5063
    Abstract: Adverse events and toxicity concerns account for roughly 50% of clinical trial failures and the majority of drug withdrawals. A large part of these toxicity effects can be explained by a drug’s molecular target, known off targets or a drug’s lack of specificity. Therefore, the understanding of a target’s potential toxicity is crucial in the drug development process, potentially informing therapeutic hypotheses and/or chemical design. Here, we introduce a machine learning model to predict a gene’s likelihood to result in patient toxicity when inhibited, independent of chemical compound information. We used information on drug approvals, discontinuations and withdrawals from the FDA to define “toxic” and “safe” targets, to be used as training data. Our model leveraged preclinical, gene-specific features that can be easily collected such as tissue expression, pathway involvement, and loss of function data, and achieves significant predictive performance (ROC = 0.80). We validated our model’s ability to discriminate between successful and unsuccessful drug candidates based on their predicted target toxicity. Furthermore, our model was able to predict differences in toxicity between gene isoforms, which were in agreement with published in vivo evidence, further highlighting its ability to be applied to guide drug development and lead optimization. By applying our model, the prediction of target-specific toxicity can guide drug screenings, prioritize candidates and avoid costly and dangerous drug failures. Citation Format: Manuel Garcia-Quismondo, Mukti Parikh, Neel Madhukar, Coryandar Gilvary. Predicting target driven toxicity for small molecule inhibitors to aid in drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5063.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2022-5063
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 7
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    Abstract 5062: Identification of synthetic lethal gene interactions to discover novel cancer vulnerabilities and guide drug development
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 5062-5062
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 5062-5062
    Abstract: Poor treatment responses in patients and drug resistance are main challenges in the clinic which can arise due an incomplete understanding of genetic dependencies within cancers and an ill defined patient population. Identifying synthetic lethal (SL) relationships between a gene pair, where loss of either of the participating genes has no effect on cancer cells, however simultaneous disruption of both genes leads to cancer cell death, can uncover major survival mechanisms and expose vulnerabilities within cancers. In spite of advances in CRISPR or RNAi based loss of function screening it is difficult to identify these gene interactions solely by experimentation due to the vast mutational landscape of cancers, inability of cell lines to capture clinically relevant mutations, and large number of possible SL gene combinations. Furthermore, large scale CRISPR/RNAi screens are prone to false positive results, leading to erroneous identification of gene interactions. We developed a machine learning model to identify SL genetic interactions by integrating pathway, ontology, genomic, and loss of function features on all gene pairs. This included features, which have been shown to be predictive of SL pairs such as, involvement in important cancer hallmark processes or signaling pathways, co-expression profile in patient cancers, and many more. The model was trained using SL pairs from published large scale experimental screens across several cancer types, ultimately enabling the prediction of cancer agnostic SL pairs. Non-SL pairs had to be computationally defined using post-perturbation gene expression profiles due to the lack of published data annotating non-SL pairs. The model showed significant predictive performance (AUROC = 0.94, AUPRC = 0.93, Specificity = 0.91), additionally, when tested on a gold standard, biologically verified set of SL gene pairs across several cancer types, the model showed good generalizability. We leveraged the model to identify key clinically relevant SL relationships and uncover complementary mechanisms tied to pro-survival processes. By identifying SL gene interacting pairs, our platform can help gain a better understanding of dominant mechanisms driving cancers and guide the discovery of novel therapies, biomarkers, and combination therapies early on during drug development. Citation Format: Mukti Parikh, Manuel García-Quismondo, Neel S. Madhukar, Coryandar Gilvary. Identification of synthetic lethal gene interactions to discover novel cancer vulnerabilities and guide drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5062.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2022-5062
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 8
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    DDIS-08. THE SMALL MOLECULE IMIPRIDONE ONC201 IS ACTIVE IN GLIOBLASTOMA WITH DRD2 PATHWAY DYSREGULATION
    Oxford University Press (OUP) ; 2017
    In:  Neuro-Oncology Vol. 19, No. suppl_6 ( 2017-11-06), p. vi60-vi60
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 19, No. suppl_6 ( 2017-11-06), p. vi60-vi60
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/nox168.244
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 2094060-9
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  • 9
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    DDIS-12. ONC201: THE FIRST SELECTIVE, NON-COMPETITIVE DRD2/3 ANTAGONIST FOR CLINICAL NEURO-ONCOLOGY
    Oxford University Press (OUP) ; 2018
    In:  Neuro-Oncology Vol. 20, No. suppl_6 ( 2018-11-05), p. vi71-vi71
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 20, No. suppl_6 ( 2018-11-05), p. vi71-vi71
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noy148.291
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
    detail.hit.zdb_id: 2094060-9
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  • 10
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    EXTH-17. SELECTIVE, NON-COMPETITIVE DRD2/3 ANTAGONISM BY IMIPRIDONE ONC206 IS EFFECTIVE IN TUMORS WITH DOPAMINE RECEPTOR DYSREGULATION
    Oxford University Press (OUP) ; 2018
    In:  Neuro-Oncology Vol. 20, No. suppl_6 ( 2018-11-05), p. vi88-vi88
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 20, No. suppl_6 ( 2018-11-05), p. vi88-vi88
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noy148.366
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
    detail.hit.zdb_id: 2094060-9
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