Context-specific Drug Research Articles

Evidence-based cost estimation of essential medicines for pediatric cancer care in Peru.

e18533 Background: Inequitable access to essential medicines is a large contributor to the inferior outcomes experienced by children with cancer living in low-and-middle-income countries. In collaboration with the Peruvian Ministry of Health and the Pan American Health Organization, we aimed to predict annual aggregate drug costs as well as primary cost drivers for treating Peruvian children with cancer to inform more accurate and efficient drug procurement. Methods: FORxECAST is a pediatric cancer-specific model that projects required drug quantity and cost for 18 common pediatric cancers, drawing on internationally adopted standard treatment protocols. It is customizable to geographic region, local cancer incidence, stage distribution, and domestic drug prices. To estimate aggregate costs utilizing FORxECAST, Peruvian incidence, stage at diagnosis, and per-unit drug prices were provided by local stakeholders. FORxECAST-embedded standardized data for country-specific age, sex, and BSA sourced by the World Health Organization and the Global Childhood Cancer Microsimulation Model were used. To provide comparative analyses, aggregate costs adapted for Peruvian incidence were calculated utilizing reference drug prices available through the Management Sciences for Health (MSH) International Medical Products Price Guide. Results: FORxECAST projected Peruvian aggregate drug costs to be USD$1,672,795.47 annually, which was 47% less than expected by median MSH prices and represents 4% of the total Peruvian cancer care budget. 19 of 22 medicines were cheaper on a per-unit basis relative to MSH. Dactinomycin was the costliest per-unit agent but contributes to less than 2% of the overall budget due to low aggregate demand. Primary cost drivers were 6-mercaptopurine, intravenous methotrexate, and asparaginase, due to the large quantities needed for treatment of Peru’s most common pediatric cancer: acute lymphoblastic leukemia. However, because of their low per-unit prices, sensitivity analyses showed that the annual drug budget would remain lower than predicted by MSH, even if the per-unit price for these 3 agents increased by 50% (USD$2,431,550.71 vs. USD$3,099,401.31). Conclusions: The cost of procuring accurate quantities of essential pediatric cancer medicines to meet population-level need is a small percentage of the total cancer budget for the Peruvian healthcare system. Given the potential impact that improved drug access could have on pediatric cancer survival, these data can justify health systemic efforts to effectively earmark funds for pediatric cancer care and minimize risks of stockout. Our analyses also demonstrate the importance of incorporating context-specific drug prices when estimating domestic budget impact. Next steps include comparative analyses with neighboring health systems in Latin America to assess regional price variation and inform opportunities for pooled procurement.

Using Geographically Weighted Regression to Explore County Subdivision Level Predictors of Drug Overdose Death in Connecticut, U.S.

Deaths caused by drug overdoses have increased significantly over the past 2 decades in the U.S, becoming a public health concern. Existing empirical evidence examining the spatial association between the contextual correlates and drug overdose death rates, however, remains limited and ambiguous. Additionally, death caused by drug overdose is a multi-disciplinary issue and requires a correspondingly multifaceted and multidisciplinary approach, but there has been little research to date in the U.S. focusing on risk factors of drug overdose deaths from a crime perspective. This paper uses geographically weighted regression to examine the relationship between drug overdose death rates and contextual characteristics at the county subdivision level in the State of Connecticut. The results show that explanatory variables, such as gender, education, poverty, housing, and racial/ethnic diversity, are associated with drug overdose death rates in the state. Most importantly, the association between drug overdose death rates and all explanatory variables in our analysis significantly varied over space, highlighting the need for local and context-specific drug overdose prevention and intervention programs. In addition, this research enables health practitioners, policy makers, and police to gain a better understanding of the geography of drug overdose victimization and efficiently allocate resources to battle drug overdose deaths.

A Patient-to-Model-to-Patient (PMP) Cancer Drug Discovery Protocol for Identifying and Validating Therapeutic Agents Targeting Tumor Regulatory Architecture.

The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six-step patient-to-model-to-patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context-specific drug mechanism of action (MOA) and permit precise, biomarker-based preclinical validations of drug efficacy. The combination of determination of a drug's context-specific MOA and precise identification of patients' tumor checkpoints provides a personalized, mechanism-based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Abstract 1053: A large cellular screen charting the landscape of synergistic drug combinations in lung cancer

Abstract Introduction Non-small-cell lung cancer (NSCLC) is among the most common cancers, accounting for 85% of all lung cancer cases. Drug combinations are known to more effective than single drugs and can delay drug resistance. NSCLC has a poor prognosis and there are no approved targeted cancer drug combinations. Here we have conducted the first large-scale screen of drug combinations in NSCLC cell lines to identify new candidate synergistic combinations and map the landscape of synthetic lethal and synergistic pathways underlying them. Results We conducted the largest of its kind in vitro drug combination screening of 81 NSCLC cell-lines for 21 x 242 cancer drug combinations at 5 different doses. Our collection of approved and investigational drugs included mainly targeted drugs as well as some chemotherapy drugs. For each drug combination, we computed the synergy score using the Bliss model. Our key findings are as follows: 1. We identify drug combinations (like CHEK and WEE1 inhibitors) that are highly synergistic in many NSCLC cell lines. 2. Combinations of targeted therapies are overall more synergistic than chemotherapeutic drug combinations. 3. Synergistic combinations occur more within pathways. 4. We see that most drug combinations do not make cell lines more sensitive than the most sensitive cell line to the corresponding single agents across all the cell lines tested. 5. We identify a new class of drug combinations - called super-sensitizers - whose combined effects are larger than the effects observed for the individual drugs composing across all of the cohort cell-lines. 6. Combinations of multi-target drugs tend to be more synergistic than single-target drugs. 7. We identify and characterize a set of context-specific drug combinations that are differentially synergistic or differentially effective (i.e. viability after treatment) between wild-type vs mutant cell lines of various cancer drivers (KRAS, TP53, STK11, PIK3CA, EGFR). 8. Studying potential mechanistic underpinnings of drug synergism in NSCLC, we find that: (a) synergistic combinations target genes that have more proximal physical interactions, (b) some synergistic combinations target synthetic lethal combinations, (c) a subset of synergistic combinations have further support by NSCLC patients survival analysis. Conclusions We performed the first large in vitro drug combination screen in NSCLC. We identify top drug combinations that are highly synergistic and effective in many cell lines and characterize key biological properties and mechanisms underlying them. The screen results are provided in a user-friendly public data resource that can help facilitate the development of targeted drug combinations in lung cancer therapy. Citation Format: Nishanth Ulhas Nair, Adam Friedman, Patricia Greninger, Avinash D. Sahu, Ellen Murchie, Giovanna Stein Crowther, Joe McClanaghan, Joo Sang Lee, Daniel A. Haber, Jeffrey Engelman, Eytan Ruppin, Cyril Benes. A large cellular screen charting the landscape of synergistic drug combinations in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1053.

A Community Challenge for Pancancer Drug Mechanism of Action Inference from Perturbational Profile Data

The Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams who were asked to predict high-affinity binding target among 255 possible protein kinases. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects

We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through higher-order tensors, and efficiently learns latent factors of the tensor using powerful factorization machines. The approach enables comboFM to leverage information from previous experiments performed on similar drugs and cells when predicting responses of new combinations in so far untested cells; thereby, it achieves highly accurate predictions despite sparsely populated data tensors. We demonstrate high predictive performance of comboFM in various prediction scenarios using data from cancer cell line pharmacogenomic screens. Subsequent experimental validation of a set of previously untested drug combinations further supports the practical and robust applicability of comboFM. For instance, we confirm a novel synergy between anaplastic lymphoma kinase (ALK) inhibitor crizotinib and proteasome inhibitor bortezomib in lymphoma cells. Overall, our results demonstrate that comboFM provides an effective means for systematic pre-screening of drug combinations to support precision oncology applications.

Abstract 5399: The NSD2 p.E1099K mutation is enriched at relapse and confers drug resistance in a cell context dependent manner in pediatric acute lymphoblastic leukemia

Abstract Introduction: Outcomes for children with relapsed acute lymphoblastic leukemia (ALL) remain poor. Mutations in epigenetic modifiers are commonly found at relapse, including the activating p.E1099K (EK) mutation within the conserved SET domain of NSD2, which has been reported in 10% of patients at relapse. NSD2 has 3 isoforms, two of which (Types II and REIIBP) contain the SET domain. Our aim was to determine the role of NSD2 in driving relapse and further elucidate the prevalence of NSD2 EK in patient samples. Methods: Whole genome/exome sequencing was performed on diagnosis/relapse ALL pairs and data were aggregated for somatic SNVs/indels. NSD2 EK positive cases at relapse were subject to ultra-deep targeted re-sequencing to determine the presence of small subclones at diagnosis. To investigate the role of NSD2 and its isoforms in mediating relapse, short hairpin RNAs (shRNAs) were used to knockdown (KD) NSD2 in three NSD2 EK mutant cell lines with non-targeting (NT) shRNA used as controls. Cells were treated with chemotherapy commonly used in pediatric ALL and assessed for cytotoxicity by CellTiter- Glo®. To determine NSD2's impact on gene expression and the epigenetic landscape, RNAseq, ChIPseq, ATACseq, and Hi-C were performed using standard techniques. Results: Examination of paired ALL patient samples revealed enrichment of NSD2 EK clones at relapse. B-ALL cell lines heterozygous for NSD2 EK (RS4;11, RCH-ACV, SEM) revealed that KD led to decreased proliferation in all lines and decreased clonal growth in RCH-ACV relative to NT. Furthermore, increased sensitivity to various chemotherapeutic agents was observed in the KD lines compared to NT controls, but the pattern of drug sensitivity varied among lines implying cell context specific drug responses. Simultaneous knockdown of Type II and REIIBP had a greater impact than Type II alone, indicating both SET containing EK isoforms contribute to phenotypic changes. When comparing gene expression changes in NT vs KD, minimal overlap in differential genes was noted among the three cell lines (4.4% upregulated, 1.7% downregulated) indicating significant diversity in transcriptional reprogramming. ATACseq of the NT and KD for all three lines revealed intergenic regions as the most affected by NSD2 with RCH-ACV and RS4;11 NT lines having more open chromatin compared to their KD whereas SEM NT displayed marked peak loss compared to its KD. Less than 2% of differential intergenic regions were shared among the lines. Hi-C analysis demonstrated disparity in chromosome architecture among NSD2 EK lines as well. Shifts in intergenic regions was further supported by ChIPseq, which revealed increased H3K36me2 across intergenic regions in RS4;11 NT compared to KD. Lastly, we noted a strong correlation of increased gene expression with increased H3K36me2, H3K9ac, and H3K27ac at promoters and gene bodies. Conclusions: NSD2 EK plays a unique role in mediating clonal fitness through pleiotropic mechanisms that are dependent on the underlying cellular context. Citation Format: Joanna Pierro, Jason Saliba, Sonali Narang, Gunjan Sethia, Shella Saint Fleur-Lominy, Ashfiyah Chowdhury, Takaya Moriyama, Kjeld Schmiegelow, Jun J. Yang, Mignon L. Loh, Patrick A. Brown, Jinghui Zhang, Xiaotu Ma, Aristotelis Tsirigos, Nikki A. Evensen, William L. Carroll. The NSD2 p.E1099K mutation is enriched at relapse and confers drug resistance in a cell context dependent manner in pediatric acute lymphoblastic leukemia [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 5399.

Coverage of mass drug administration for elimination of lymphatic filariasis in urban Nagpur, Central India: A mixed method study.

Background and Aims:Mass Drug Administration (MDA) coverage remains an important indicator in elimination of Lymphatic Filariasis (LF), especially in context of recent changes in programme strategies in India, such as incorporation of Ivermectin and involvement of urban Accredited Social Health Activists (ASHAs). This study aimed at assessing the coverage and compliance with MDA of Filariasis as well as exploring perspective of beneficiaries for non-consumption in selected slum area of Nagpur city.Methods:Mixed-method study design comprising of quantitative assessment of MDA coverage, followed by qualitative method to explore the reasons of non-compliance was used in selected slum areas of Nagpur city. Using cluster sampling, 240 households were selected and house-to-house visits were made to interview the eligible participants. In-depth interviews were conducted among selected non-compliant participants.Statistical Analysis:Multivariable logistic regression analysis to identify the factors associated with non-consumption. Thematic analysis was done to obtain the reasons of non-consumption as perceived by the beneficiaries.Results:Among the 1096 individuals studied, distribution and consumption coverage were 55.2% and 48.5%, respectively. Effective supervised consumption was further low (28.9%). Coverage compliance gap (CCG) was 12.1%. Male sex and younger age (2-5 years) were significant socio-demographic determinants of non-consumption. No repeat visit to houses left in first round, fear of side effects, pill burden, poor understanding about the need were important reasons as revealed by qualitative inquiry.Conclusion:Effective pre-campaign awareness, incorporation of context specific drug delivery strategies and strengthening monitoring system are essential for successful MDA implementation.

Abstract 3896: A big data approach to predicting context specific synergistic drug combinations

Abstract Combination therapies for various cancers have been shown to increase efficacy, lower toxicity and escape resistance. However, systematically interrogating all possible synergistic therapies is experimentally unfeasible due to the sheer volume of possible combinations. Computational approaches have proven to be an invaluable tool within pharmacogenomics and have helped with prioritizing the development of perspective therapeutics as well as matching the right drugs with the right patients. Here we apply a novel big data approach in the evaluation and ultimately the prediction of drug synergy by using the recently released NCI-ALMANAC, the largest publically available synergistic drug efficacy dataset to date. First, to better understand drug combinations, we distinguished between those that were synergistic and adverse and evaluated various drug similarity metrics for all pairs. We found that certain features showed significant differences between adverse and synergistic drug combinations, such as post-treatment transcriptional effects similarity (D = 0.17, p < 0.001) and chemical structure similarity (D = 0.25, p < 0.001). By exploiting these significant similarities and dissimilarities and incorporating cell line specific data we developed a machine learning model to predict context specific drug synergy and achieved significant predictive performance (AUC = 0.823). We find that our model can be used to both identify novel synergistic drug pairs, as well as find novel indications for known drug combinations by identifying new sensitive cell lines. Moreover, the interpretability of our model allows for the interrogation of features for a deeper understanding of why certain combinations are predicted synergistic. In addition to identifying the cancer types and subtypes a combination therapy would be most synergistic within, we set out to identify the molecular indication for highly synergistic pairs. Specifically, we systematically identified candidate predictive biomarkers which could be used to stratify patient cohorts. Overall, our model and methodology can expedite the development and expansion of combination therapeutics, which can help battle acquired resistance and increase therapeutic efficacy. The thorough understanding of specific combination efficacy dependencies allows for a true precision medicine application of these therapeutics. Citation Format: Coryandar M. Gilvary, Neel Madhukar, Olivier Elemento. A big data approach to predicting context specific synergistic drug combinations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3896.

Abstract 4383: SOCRATES: integrating ex vivo and in silico analysis to identify optimal drug combinations for patients

Abstract Recent work on cancer heterogeneity and evolution has greatly enhanced our understanding of drivers of drug resistance, but this has also highlighted the need to move away from generic, “one-size-fits-all” treatment regimes. It is clear that we should tailor therapy to individual patients at different stages of disease based on the current behaviour of their tumour cells, yet detailed experimental characterisation and analysis of patient tissue is impractical and costly. Predictive computational models that can produce patient-specific recommendations of drug combinations would therefore be of great value to the field of personalised medicine. In silico modelling has already been used to successfully predict synergistic drug combinations in single cell lines in the lab, but such approaches have not yet been translated to dynamic, real patient data in the clinic. The SOCRATES project (Signalling output to rationalise combinations of targeted anticancer therapies) integrates experimental data and computational analysis to predict context-specific synergistic drug combinations. Here we present ambitious computational modelling of dynamic signalling network changes in response to ex vivo exposure of seven targeted drugs used singly at clinically appropriate concentrations. We also apply our adaptive modelling to genomics and proteomics data from 54 proteins in over 30 non small-cell lung cancer cell lines and ten patient samples to predict and experimentally validate personalised, context specific drug combinations. We will describe our adaptive, in silico modelling for predicting response to drug combinations, including novel methods utilizing network topology. We have predicted over 50 synergistic drug combinations and will present our predictions alongside initial results of experimental validation in the lab. We will also illustrate the differences observed in various cell lines and patient derived cells depending on genetic context. SOCRATES is, to our knowledge, the first project of its kind and is a prototype for the future of adaptive, individualised cancer treatment. Citation Format: Elizabeth A. Coker, Adam Stewart, Anna Minchom, Mary O’Brien, Timothy Yap, Paul Workman, Udai Banerji, Bissan Al-Lazikani. SOCRATES: integrating ex vivo and in silico analysis to identify optimal drug combinations for patients. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4383.

Pilot evaluation of an optimized context-specific drug–drug interaction alerting system: A controlled pre-post study

ObjectivesClinical decision support (CDS) systems are frequently used to reduce unwanted drug–drug interactions (DDIs) but often result in alert fatigue. The main objective of this study was to investigate whether a newly developed context-specific DDI alerting system would improve alert acceptance. MethodsA controlled pre-post intervention study was conducted in 4 departments in a university hospital. After a 7-month pre-intervention period, the new system was activated in the intervention departments, while the old system remained activated in the control departments. Post-intervention data was collected for a 7-month period. ResultsA significant increase of the overall acceptance rate was observed between the pre- and post-intervention period (2.2% versus 52.4%; p<0.001) for the intervention departments and between the intervention and control departments (2.5% versus 52.4%; p<0.001) in the post-intervention period. There were no significant differences in acceptance rates between the pre- and post-intervention period in the control departments and also not between the control and intervention departments in the pre-intervention period. ConclusionsThe improvement was probably related to several optimization strategies including the customization of the severity classification, the creation of individual screening intervals, the inclusion of context factors for risk assessment, the new alert design and the creation of a follow-up system. The marked increase in alert acceptance looks promising and should be further evaluated after hospital wide implementation. System aspects that require further optimization were identified and will be developed. Further research is warranted to develop context-aware algorithms for complex class–class interactions.

If You Want It, You Can Get It Right Here

There is a burgeoning body of sociological literature that focuses on strip clubs and the club actors therein; most notably, strippers. Most of this scholarly work has emphasized interactions between strippers and customers, the deployment of stigma management strategies in order to neutralize deviantizing interactions and identities, gender performances and hierarchies, power, inequality and social control, and socialization processes associated with becoming dancers. While scholars have paid some attention to drugs and alcohol in the locale, they have paid little attention to how drug and alcohol use relates to the spatial organization and material culture of the strip club, the relationship between stigma and where club actors use illicit drugs, and how using certain drugs contributes to discreditable identities of already stigmatized workers. By centralizing accounts of setting actors through interviewing to supplement my complete participant fieldworker role, I build ideas about context-specific drug and alcohol use, power, and the ecology of drugs.

Abstract 5236: Novel network analysis framework identifies context-specific drug combinations in melanoma.

Abstract Gene expression data can be used to identify the activation status of genes and pathways in tumor samples and to infer the structure of regulatory networks. Most tumor datasets such as The Cancer Genome Atlas focus on unperturbed samples, and subsequently, the statistical frameworks that analyze them. We hypothesized that perturbation of cells can uncover network structures that are undetectable in basal state, and reveal the complexity of cancer cells. To demonstrate this, we generated gene expression data from a panel of melanoma cell lines before and after inhibition of MEK or AKT. Here we present a new computational framework to analyze gene expression data before and after perturbations. The algorithm aims to define “differential networks1” - rewiring of network components in different tumors. The algorithm sheds light on the complex and context-specific nature of network structure, including pathways that are activated, or under the control of MAPK or AKT, only in a subset of samples. The perturbation data allowed us to assess pathway activation levels that are undetectable in unperturbed state. For example, AKT is activated in melanoma by PTEN loss, but the effect of NRAS mutation on AKT is unknown, and AKT activation is undetectable in unperturbed cells. Our algorithm identified an AKT activation signature in perturbed state, and helped define the effects of PTEN and NRAS mutations on the pathway. Additionally, the algorithm inferred the genetic status of p53 in each cell line by identifying pathway activation, changes that are, once again, detectable only after perturbations. By linking protein data with the algorithm's results, we identified cell lines with constitutive activation of the STAT3 pathway, and demonstrated the crosstalk between MAPK and STAT3 pathways. Moreover, combining the results from the algorithm with known signatures of drug-sensitivity accurately predicted which drug combinations provided a synergistic effect in each cell line. Associations between pathway activity level and drug sensitivity are known for some drugs. By using our algorithm's ability to infer pathway status in different tumors and to detect changes in those pathways after drug treatment, it correctly predicted when drug treatment sensitized cells to the effects of chemotherapy and other drugs. To conclude, we show that gene expression of perturbed cells greatly enhances the power of network structure analysis. Our results reveal context-specific changes in network structure, and predict which cell lines are sensitized to specific drugs by pathway perturbations. These results have direct applicability to the development of drug combinations in melanoma. 1 Pe'er and Hacohen, Cell 2011 Citation Format: Oren Litvin, Sarit Schwartz, Mark Rocco, Tanya Schild, Neal Rosen, Dana Pe'er. Novel network analysis framework identifies context-specific drug combinations in melanoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5236. doi:10.1158/1538-7445.AM2013-5236 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.

Contested identities: Identity constructions in a youth recreational drug culture

This article examines young recreational drug users’ identity constructions. Combining a poststructuralist theoretical framework with focus group method, the article investigates how the participants in a focus group interview position themselves and others, thereby negotiating an identity as responsible drug users. The article studies this recreational drug culture and its internal distinctions, conceptions and norms as they are expressed discursively. The analysis identifies six dimensions of the identity as a responsible, recreational drug user: drug practice, general drug knowledge, context-specific drug knowledge, practices for checking drugs, acknowledging one’s position in the surrounding drug scene and age. The analysis shows how being able to perform a coherent identity in line with these dimensions is necessary for being acknowledged as a responsible, recreational drug user.

Combined Session With American Association of Electromyography and Electrodiagnosis

The Columbia Cancer Target Discovery and Development (CTD²) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for <i>de novo</i> drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams who were asked to predict high-affinity binding target among 255 possible protein kinases. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.