Prioritization Of Drugs Research Articles

Predicted environmental concentration (PEC), environmental risk assessment (ERA) and prioritization of antiretroviral drugs (ARVs) in seawater from Guarujá (Brazilian coastal zone)

Predicted environmental concentration (PEC), environmental risk assessment (ERA) and prioritization of antiretroviral drugs (ARVs) in seawater from Guarujá (Brazilian coastal zone)

The New Model of Prioritizing Orphan Drugs: The Case Study for Hemophilia, SMA, CF and MS

Background: Rare diseases, characterized by low prevalence and high complexity, pose significant challenges to health systems due to the uncertainty surrounding the best diagnostic methods and availability of effective treatments. Objectives: This study aimed to introduce new methods for prioritizing orphan drugs, with a pilot application to hemophilia, spinal muscular atrophy (SMA), cystic fibrosis (CF), and multiple sclerosis (MS). Methods: This quantitative research, conducted at Iran’s National Institute for Health Research from 2021 to 2023, employs multi-criteria decision-making models (MCDA) to evaluate the efficacy of health care technologies for "rare and hard to cure" diseases. A preliminary model was developed based on a pilot selection of seven medications, for which comprehensive data was extracted. The clinical efficacy was assessed using quality adjusted life years (QALY) as a metric. The model design was grounded on three critical factors: The annual cost of intervention per individual, the annual number of eligible patients for intervention, and the Proportion of expenses covered by the governmental budget. Additionally, the model incorporated various constraints and a regulatory coefficient, denoted as “w,” to enhance its robustness. Results: By running the model, the coverage of selected medications through model optimization, revealing the following percentages: Alemtuzumab (30%), ocrelizumab (30%), emicizumab (6%), dornase alfa (29%), tobramycin (2%), and spinraza (0.02%). Additionally, the corresponding monetary coverage in Iranian Rials is reported as follows: Alemtuzumab (27,144,000,000 IRR), ocrelizumab (109,645,200,000 IRR), emicizumab (17,360,490,000 IRR), dornase alfa (43,350,930,000 IRR), tobramycin (1,268,505,000 IRR), and spinraza (350,000,000 IRR). Conclusions: This model has tried to solve the shortcomings of the existing models regarding the prioritization of orphan drugs by combining different factors to improve access to essential treatments for rare diseases, although it can be generally asserted that no unique model can answer all policymakers’ questions regarding budget allocation of rare diseases and orphan products.

Reliable anti-cancer drug sensitivity prediction and prioritization

The application of machine learning (ML) to solve real-world problems does not only bear great potential but also high risk. One fundamental challenge in risk mitigation is to ensure the reliability of the ML predictions, i.e., the model error should be minimized, and the prediction uncertainty should be estimated. Especially for medical applications, the importance of reliable predictions can not be understated. Here, we address this challenge for anti-cancer drug sensitivity prediction and prioritization. To this end, we present a novel drug sensitivity prediction and prioritization approach guaranteeing user-specified certainty levels. The developed conformal prediction approach is applicable to classification, regression, and simultaneous regression and classification. Additionally, we propose a novel drug sensitivity measure that is based on clinically relevant drug concentrations and enables a straightforward prioritization of drugs for a given cancer sample.

Abstract 933: Single-cell inference and validation of heterogeneous drug sensitivity in cholangiocarcinoma reveals dacinostat as novel therapeutic candidate

Abstract Cholangiocarcinoma is a rare, aggressive malignancy with limited treatment options, due to a paucity of actionable mutations and low response to immune checkpoint inhibitors. Furthermore, its extreme heterogeneity prevents identification of actionable dependencies from bulk-tissue profiles, such that transformed cells themselves represent as little as 10% of cells comprising the complex micro-environment of these tumors. To address these challenges, we introduce a highly generalizable, single-cell framework for the mechanism-based prioritization of drugs to treat rare, heterogeneous tumors. Analysis of tumor cells isolated by single-cell RNA-Sequencing across six patients and two independent data sources using protein activity inference has revealed three molecularly distinct subphenotypes of cholangiocarcinoma cells, present to varying degrees across patients but with highly conserved proteomic signatures, and predicted to be sensitive to four drugs by regulatory network analysis. A seventh independent patient recapitulated these phenotypes, and drug sensitivity predictions at single-cell level for this patient were validated in low-passage, patient-derived xenografts from the same patient, which confirmed tumor growth rate control by two of these drugs (plicamycin and dacinostat) and further validated predicted subpopulation-specific effects, such that dacinostat alone inhibited tumor growth across all three tumor subphenotypes, while plicamycin preferentially inhibited some but not all, allowing the remaining phenotypes to reconstitute a treatment-resistant tumor. This approach suggests dacinostat as a promising candidate for follow-up clinical trials in cholangiocarcinoma, either alone or in combination with current standard-of-care chemotherapies, and is highly generalizable to elucidate complementary dependencies of other aggressive, heterogeneous tumors. Citation Format: Aleksandar Obradovic, Lorenzo Tomassoni, Daoqi Yu, Charles G. Drake, FIlemon Dela Cruz, Andrew Kung, Andrea Califano. Single-cell inference and validation of heterogeneous drug sensitivity in cholangiocarcinoma reveals dacinostat as novel therapeutic candidate [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 933.

Integrative analysis of functional genomic screening and clinical data identifies a protective role for spironolactone in severe COVID-19

We demonstrate that integrative analysis of CRISPR screening datasets enables network-based prioritization of prescription drugs modulating viral entry in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by developing a network-based approach called Rapid proXimity Guidance for Repurposing Investigational Drugs (RxGRID). We use our results to guide a propensity-score-matched, retrospective cohort study of 64,349 COVID-19 patients, showing that a top candidate drug, spironolactone, is associated with improved clinical prognosis, measured by intensive care unit (ICU) admission and mechanical ventilation rates. Finally, we show that spironolactone exerts a dose-dependent inhibitory effect on viral entry in human lung epithelial cells. Our RxGRID method presents a computational framework, implemented as an open-source software package, enabling genomics researchers to identify drugs likely to modulate a molecular phenotype of interest based on high-throughput screening data. Our results, derived from this method and supported by experimental and clinical analysis, add additional supporting evidence for a potential protective role of the potassium-sparing diuretic spironolactone in severe COVID-19.

Occurrence, ecological risk assessment and prioritization of pharmaceuticals and abuse drugs in estuarine waters along the São Paulo coast, Brazil.

The pollution of the surface waters by pharmaceuticals and personal care products (PPCPs) has attracted worldwide attention, but data regarding their occurrence and potential risks for the aquatic biota on tropical coastal rivers of South America are still scarce. In this context, the occurrence and the preliminary ecological risk assessment of eleven pharmaceuticals of various therapeutic classes (including cocaine and its primary metabolite, benzoylecgonine) were investigated, for the first time, in five rivers of São Paulo, southeast Brazil, covering a coastline of about 140 km, namely Perequê River, Itinga River, Mongaguá River, Itanhaém River and Guaraú River. Although these five rivers are born in well-preserved areas of the Atlantic rainforest biome, on its way to sea and when they cross the urban perimeter, they receive untreated sewage discharges containing a complex mixture of contaminants. In addition, a “persistence, bioaccumulation and toxicity” (PBT) approach allowed to pre-select the priority PPCPs to be monitored in this coastline. Identification of several PPCPs in the samples was done using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Ten PPCPs were successfully quantified in all five rivers, namely caffeine (9.00–560.00 ng/L), acetaminophen (<LOQ–22.24 ng/L), benzoylecgonine (0.30–14.93 ng/L), atenolol (0.12–13.22 ng/L), losartan (0.10–8.42 ng/L), diclofenac (0.76–3.93 ng/L), cocaine (0.05–3.22 ng/L), furosemide (<LOQ–3.16), carbamazepine (0.04–0.50 ng/L) and orphenadrine (<LOQ–0.14 ng/L). From an ecological risk perspective, caffeine, acetaminophen and losartan can be considered as priority PPCPs because they showed low to moderate risks to algae, crustacean and fishes. However, using the PBT approach, carbamazepine and orphenadrine were also classified as priority compounds, followed by furosemide, acetaminophen, cocaine and losartan (all in second position) and caffeine, atenolol, diclofenac and benzoylecgonine (all in third position). This study provides valuable information to reinforce the importance of continuous monitoring of the coastal rivers of South America (containing PPCPs and illicit drugs) whose diffuse loads flow continuously into the marine ecosystems. Furthermore, ecotoxicological studies (especially with tropical marine organisms) to assess the long-term toxicity of these bioactive compounds are urgent.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-21945-w.

Ontology-based identification and prioritization of candidate drugs for epilepsy from literature

BackgroundDrug repurposing can improve the return of investment as it finds new uses for existing drugs. Literature-based analyses exploit factual knowledge on drugs and diseases, e.g. from databases, and combine it with information from scholarly publications. Here we report the use of the Open Discovery Process on scientific literature to identify non-explicit ties between a disease, namely epilepsy, and known drugs, making full use of available epilepsy-specific ontologies.ResultsWe identified characteristics of epilepsy-specific ontologies to create subsets of documents from the literature; from these subsets we generated ranked lists of co-occurring neurological drug names with varying specificity. From these ranked lists, we observed a high intersection regarding reference lists of pharmaceutical compounds recommended for the treatment of epilepsy. Furthermore, we performed a drug set enrichment analysis, i.e. a novel scoring function using an adaptive tuning parameter and comparing top-k ranked lists taking into account the varying length and the current position in the list. We also provide an overview of the pharmaceutical space in the context of epilepsy, including a final combined ranked list of more than 70 drug names.ConclusionsBiomedical ontologies are a rich resource that can be combined with text mining for the identification of drug names for drug repurposing in the domain of epilepsy. The ranking of the drug names related to epilepsy provides benefits to patients and to researchers as it enables a quick evaluation of statistical evidence hidden in the scientific literature, useful to validate approaches in the drug discovery process.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19.

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

HOW MACHINE LEARNING CAN ENHANCE CLINICAL DEVELOPMENT

Deep learning models on medical images are now widely used to capture signals regarding patients’ outcomes. In this talk, we discuss how AI models can improve the clinical development pipeline by reducing the time to market and enhancing prioritization of drugs using external datasets. In oncology, both the sample size requirements and the duration are keys for phase III trials. Recent randomized trials in newly diagnosed patients with DLBCL have taken on average more than 5 years between first enrollment and publication [Batlevi2018]. We will present how this can be reduced by adjusting the primary analysis on deep learning predictions of the outcome. Another challenge in clinical development is the decision whether to launch a phase III trial based on the results of a single-arm short-term phase II trial. This decision process can be enriched by an estimation of treatment effect based on the single-arm data and historical controls. We will discuss how recently developed synthetic control arm methods, which rely on machine learning, allow more precise estimation of treatment effect. Additionally, to overcome the short-term follow-up in early phases, we will discuss how digital surrogate endpoints, trained on previous phase III trials, can help to extract information on efficacy. Keywords: Bioinformatics; Computational and Systems Biology Conflicts of interests pertinent to the abstract P. Trichelair Employment or leadership position in a company: Owkin France SESSION 1: NEW THERAPEUTICS

Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer\u2019s disease

BackgroundIdentifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective.MethodsIn this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes.ResultsWe applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib).ConclusionsOur deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

MicroRNA-Mediated Drug Repurposing Unveiled Potential Candidate Drugs for Prolactinoma Treatment

Introduction: Prolactinomas, also called lactotroph adenomas, are the most encountered type of hormone-secreting pituitary neuroendocrine tumors in the clinic. The preferred first-line therapy is a medical treatment with dopamine agonists (DAs), mainly cabergoline, to reduce serum prolactin levels, tumor volume, and mass effect. However, in some cases, patients have displayed DA resistance with aggressive tumor behavior or are faced with recurrence after drug withdrawal. Also, currently used therapeutics have notorious side effects and impair the life quality of the patients. Methods: Since the amalgamation of clinical and laboratory data besides tumor histopathogenesis and transcriptional regulatory features of the tumor emerges to exhibit essential roles in the behavior and progression of prolactinomas; in this work, we integrated mRNA- and microRNA (miRNA)-level transcriptome data that exploit disease-specific signatures in addition to biological and pharmacological data to elucidate a rational prioritization of pathways and drugs in prolactinoma. Results: We identified 8 drug candidates through drug repurposing based on mRNA-miRNA-level data integration and evaluated their potential through in vitro assays in the MMQ cell line. Seven repurposed drugs including 5-fluorocytosine, nortriptyline, neratinib, puromycin, taxifolin, vorinostat, and zileuton were proposed as potential drug candidates for the treatment of prolactinoma. We further hypothesized possible mechanisms of drug action on MMQ cell viability through analyzing the PI3K/Akt signaling pathway and cell cycle arrest via flow cytometry and Western blotting. Discussion: We presented the transcriptomic landscape of prolactinoma through miRNA and mRNA-level data integration and proposed repurposed drug candidates based on this integration. We validated our findings through testing cell viability, cell cycle phases, and PI3K/Akt protein expressions. Effects of the drugs on cell cycle phases and inhibition of the PI3K/Akt pathway by all drugs gave us promising output for further studies using these drugs in the treatment of prolactinoma. This is the first study that reports miRNA-mediated repurposed drugs for prolactinoma treatment via in vitro experiments.

SubtypeDrug: a software package for prioritization of candidate cancer subtype-specific drugs.

Cancer can be classified into various subtypes by its molecular, histological or clinical characteristics. Discovering cancer-subtype-specific drugs is a crucial step in personalized medicine. SubtypeDrug is a system biology R-based software package that enables the prioritization of subtype-specific drugs based on cancer expression data from samples of many subtypes. This provides a novel approach to identify the subtype-specific drug by considering biological functions regulated by drugs at the subpathway level. The operation modes include extraction of subpathways from biological pathways, identification of dysregulated subpathways induced by each drug, inference of sample-specific subpathway activity profiles, evaluation of drug-disease reverse association at the subpathways level, identification of cancer-subtype-specific drugs through subtype sample set enrichment analysis, and visualization of the results. Its capabilities enable SubtypeDrug to find subtype-specific drugs, which will fill the gaps in the recent tools which only identify the drugs for a particular cancer type. SubtypeDrug may help to facilitate the development of tailored treatment for patients with cancer. The package is implemented in R and available under GPL-2 license from the CRAN website (https://CRAN.R-project.org/package=SubtypeDrug). Supplementary data are available at Bioinformatics online.

An Integrated Approach to Identify New Anti-Filarial Leads to Treat River Blindness, a Neglected Tropical Disease.

Filarial worms cause multiple debilitating diseases in millions of people worldwide, including river blindness. Currently available drugs reduce transmission by killing larvae (microfilariae), but there are no effective cures targeting the adult parasites (macrofilaricides) which survive and reproduce in the host for very long periods. To identify effective macrofilaricides, we carried out phenotypic screening of a library of 2121 approved drugs for clinical use against adult Brugia pahangi and prioritized the hits for further studies by integrating those results with a computational prioritization of drugs and associated targets. This resulted in the identification of 18 hits with anti-macrofilaricidal activity, of which two classes, azoles and aspartic protease inhibitors, were further expanded upon. Follow up screening against Onchocerca spp. (adult Onchocerca ochengi and pre-adult O. volvulus) confirmed activity for 13 drugs (the majority having IC50 < 10 μM), and a counter screen of a subset against L. loa microfilariae showed the potential to identify selective drugs that prevent adverse events when co-infected individuals are treated. Stage specific activity was also observed. Many of these drugs are amenable to structural optimization, and also have known canonical targets, making them promising candidates for further optimization that can lead to identifying and characterizing novel anti-macrofilarial drugs.

Gene-signature-derived IC50s/EC50s reflect the potency of causative upstream\xa0targets and downstream phenotypes

Multiplexed gene-signature-based phenotypic assays are increasingly used for the identification and profiling of small molecule-tool compounds and drugs. Here we introduce a method (provided as R-package) for the quantification of the dose-response potency of a gene-signature as EC50 and IC50 values. Two signaling pathways were used as models to validate our methods: beta-adrenergic agonistic activity on cAMP generation (dedicated dataset generated for this study) and EGFR inhibitory effect on cancer cell viability. In both cases, potencies derived from multi-gene expression data were highly correlated with orthogonal potencies derived from cAMP and cell growth readouts, and superior to potencies derived from single individual genes. Based on our results we propose gene-signature potencies as a novel valid alternative for the quantitative prioritization, optimization and development of novel drugs.

PS4DR: a multimodal workflow for identification and prioritization of drugs based on pathway signatures

BackgroundDuring the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning.ResultsHere, we present a customizable workflow (PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs.ConclusionWe showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr.

The Parenting Burdens and Family Support Needs of the Mothers of the Young Children with Severe and Multiple Disabilities

본 연구는 뇌병변 장애를 포함한 중도&#8228;중복장애 영유아 어머니 6명을 대상으로 양육부담과 가족 지원 요구에 대하여 알아보고자 하였다. 자료 수집은 반구조화된 면담지를 사용하여 개별 심층 면담을 실시하였으며 전사된 자료는 질적 분석 방법인 연속적 비교법에 따라 분석하였다. 연구 결과는 다음과 같다. 첫째, 연구 참여자들은 자녀의 장애진단 과정에서의 경험과 양육에 대한 부담감, 중도&#8228;중복 장애아 어머니로 살아가는 고독함, 장애인 지원제도의 한계로 어려움을 겪고 있었다. 둘째, 연구 참여자들은 중도&#8228;중복장애 영유아 가정에 필요한 지원으로 돌봄 서비스와 자녀의 성장주기에 따른 지원제도의 정보제공, 장애인 지원제도에 적용하고 있는 소득 기준의 하향 조정, 중도&#8228;중복장애 학생을 배려한 학교시스템 구축, 선천성 뇌 이상에 대한 보험 적용과 중증의 희귀난치성질환자에 대한 신약 우선 제공을 요구하였다. 본 연구 결과는 중도&#8228;중복장애 영유아 어머니들의 목소리를 통해 이들이 경험하고 있는 양육 부담과 지원의 방향을 제시함으로써 중도&#8228;중복장애 영유아 가족들이 필요로 하는 실질적인 가족 지원방안 마련을 위한 기초자료를 제공하였다는 데 의의가 있다.The purpose of this study was to study the parenting burdens experienced by the mothers of young children with severe and multiple disabilities, including cerebral palsy and to find out what family support is needed for them. Data collection was conducted through individual in-depth interviews with study participants, and a semi-structured interview was prepared for the interview and the transcription data were analyzed according to the constant comparison method, which is a qualitative analysis method. The results were as follows. First, participants in the study were struggling with their experiences in the child s disability diagnosis process, the burden of parenting, the loneliness of mothers with children with severe and multiple disabilities, and the limitations of the disability support system. Second, study participants were asked to provide child care services to families with children with severe and multiple disabilities and to provide information about the support system according to the child s growth cycle. It also downgraded the income standards for the disability support system, established a school system for children with severe and multiple disabilities, covered insurance for congenital brain abnormalities, and demanded prioritization of new drugs for people with severe and rarely incurable diseases. The purpose of this study was to provide the basic data for preparing practical family support plans for the families of young children with severe and multiple disabilities by suggesting the parenting burden and effective family supports through the voices of mothers with young children with severe and multiple disabilities.

Abstract ED01-02: Successes and failures of targeted drug development in pediatric tumors

Abstract Eighty percent of children diagnosed with cancer are cured with multimodality therapy including cytotoxic chemotherapy. However, up to 40% of survivors of childhood cancer have at least one serious chronic medical condition as a consequence of curative therapy. Thus, patients, families, clinicians, researchers, and regulatory agencies enthusiastically endorse the development of targeted therapy to improve survival, decrease life threatening acute toxicity, and late effects of therapy for childhood cancer. Genomic characterization demonstrates that the tumor mutational burden in childhood cancers is low. For children with cancer a small number of drugs, such as NTRK inhibitors have had major unequivocal successes including initial regulatory approvals that include specific dosing in children. Other molecularly targeted drugs have achieved moderate success, however, many have failed despite strong pre-clinical rationale. As of June 2019, 25 drugs have been FDA approved for use in children with cancer including five tyrosine kinase inhibitors (3 BCR-ABL inhibitors and 2 NTRK inhibitors), and one mTOR inhibitor. A single drug, dinutuximab, was approved for a pediatric specific indication in high-risk neuroblastoma. This parallels recent European Medicines Agency authorizations that include indications for treating childhood cancer in 10 of 177 anti-cancer drug approvals. Similar to adults, the era of targeted therapies in children with cancer began with imatinib. Drugs targeting BCR-ABL in chronic myelogenous leukemia (CML) and Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ALL) in children include imatinib, dasatinib, and nilotinib. Point mutations in the kinase domain of the BCR-ABL fusion protein result in resistance to imatinib. Dasatinib, nilotinib, and ponatinib were developed to overcome resistance. Each has a specific role in caring for children with CML or Ph+ ALL. Ph-like ALL, a high-risk acute leukemia in children and young adults, is characterized by genomic alterations and activation of signaling pathways including JAK/STAT, amenable to inhibition tyrosine kinase inhibitors. The landmark age and histology agnostic global approval of larotrectinib and entrectinib was based on durable objective responses in patients with tumors harboring NTRK fusions enrolled on early phase clinical trials. Larotrectinib has produced dramatic objective responses in children and adults with cancer harboring NTRK fusions including a very rare cancer, infantile fibrosarcoma which is NTRK fusion positive in at least 95% cases. In addition to potent inhibition NTRK fusions, initial clinical trials of larotrectinib included adults and children as well as a pediatric appropriate formulation. Children with brain tumors harboring NTRK1,2,3 or ROS1 fusions have had rapid, durable objective responses to entrectinib. MAP kinase pathway aberrations occur in childhood cancer including rhabdomyosarcoma and brain tumors. Everolimus is approved for patients with subependymal giant cell astrocytoma. Dabrafenib and vemurafenib have produced durable objective responses in patients with pediatric low-grade glioma harboring BRAF V600E mutations. Selumetinib has been FDA approved for the treatment of plexiform neurofibromas in children with neurofibromatosis type 1 and may have activity in children and adolescents with solid tumors harboring MAP kinase aberrations. Overall, the success of MAP kinase inhibition in children with cancer has been modest, leading to consideration for combinatorial approaches. Crizotinib is approved for the treatment of ALK-positive metastatic NSCLC and has activity in children with refractory Anaplastic Large Cell Lymphoma (ALCL) and inflammatory myofibroblastic tumors, both associated with ALK fusions. Trials are ongoing to determine the activity of crizotinib and other ALK inhibitors in children with neuroblastoma with known translocations, mutations or amplifications in the ALK gene. Clinical trials of multi-targeted kinase inhibitors including cabozantinib, regorafenib, sorafenib and vandetanib have demonstrated activity in a variety of solid tumors. Clinical trials of these agents have been based on mechanistic rationale and pre-clinical evaluation rather than biomarker selection. Through large scale genomic sequencing efforts in the US and Europe actionable molecular alterations including mutations, amplifications and fusions have been identified in a variety of childhood cancers. Compared to cancers in adults, these molecular aberrations occur less frequently in most cancers in children. However, due to dramatic objective responses and the potential for improved response with decreased toxicity, efforts to evaluate and incorporate targeted agents into multimodality therapy for children with cancer remains a major focus. To date, multi-targeted kinase inhibitors and drugs that target fusion oncoproteins that are histology-agnostic oncogenic drivers in cancers that occur in both child and adults have been most successful. Understanding the characteristics of successful target- drug pairs is critically important to the prioritization of new drugs and clinical trials in children as well as the development of new therapies for pediatric specific targets. Citation Format: Elizabeth Fox. Successes and failures of targeted drug development in pediatric tumors [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr ED01-02. doi:10.1158/1535-7163.TARG-19-ED01-02

PNS143 PRIORITIZATION OF DRUGS FOR REIMBURSEMENT IN THE MALTESE HEALTH SYSTEM.

Governments often experience difficulties in striking an optimal balance between ensuring access to promising drugs while sustaining the healthcare system. We investigated, how Malta, the smallest EU country, prioritizes drugs for inclusion in the basic benefit package. Our descriptive analysis of the Maltese reimbursement system was based on policy documents and semi-structured interviews guided by the Hutton Framework. We interviewed 33 key stakeholders including policymakers, committee members, clinicians, pharmacists, hospital managers and representatives of patients, pharmaceutical industry, and procurement department. In Malta, drugs for reimbursement pass through the Health Technology Assessment (HTA) unit for drawing up an assessment report of the drug, two committees advising the Minister for Health, and a procurement unit (CPSU). The first committee, the Government Formulary List Advisory Committee (GFLAC) prioritizes the drug based on health benefits and estimated costs compared to other drugs within a clinical pathway. GFLAC advises the second committee, the Advisory Committee on Health Care Benefits (ACHCB). The ACHCB further prioritizes the drug based on affordability (i.e., budget impact), sustainability and capacity of the system. Between 2014 and 2017, the committees recommended 34% of the 190 new drugs and rejected 45%. For 21% of the drugs, the decision is pending. Drugs recommended by the ACHCB are subsequently endorsed by the minister. As final decision-maker, the minister can also prioritize clinical pathways at assessment. For example, since 2017 specific annual budgets have been allocated to oncology and diabetes. For other recommended drugs (without clinical pathways), CPSU additionally prioritizes which medicines to procure first since procurement of approved drugs often exceeds CPSU’s available budget. Decision-making at multiple levels complicates the prioritization of drugs in Malta. Pending decisions further impact the prioritization process. The main criteria for prioritisation at each level seems, however, to be budget impact and allocation of sufficient funding.

Prioritization of candidate cancer drugs based on a drug functional similarity network constructed by integrating pathway activities and drug activities.

Due to the speed, efficiency, relative risk, and lower costs compared to traditional drug discovery, the prioritization of candidate drugs for repurposing against cancers of interest has attracted the attention of experts in recent years. Herein, we present a powerful computational approach, termed prioritization of candidate drugs (PriorCD), for the prioritization of candidate cancer drugs based on a global network propagation algorithm and a drug–drug functional similarity network constructed by integrating pathway activity profiles and drug activity profiles. This provides a new approach to drug repurposing by first considering the drug functional similarities at the pathway level. The performance of PriorCD in drug repurposing was evaluated by using drug datasets of breast cancer and ovarian cancer. Cross‐validation tests on the drugs approved for the treatment of these cancers indicated that our approach can achieve area under receiver‐operating characteristic curve (AUROC) values greater than 0.82. Furthermore, literature searches validated our results, and comparison with other classical gene‐based repurposing methods indicated that our pathway‐level PriorCD is comparatively more effective at prioritizing candidate drugs with similar therapeutic effects. We hope that our study will be of benefit to the field of drug discovery. In order to expand the usage of PriorCD, a freely available R‐based package, PriorCD, has been developed to prioritize candidate anticancer drugs for drug repurposing.

Prioritization of Drugs for Fungal Keratitis Eye Infections: An In-Silico Analysis

The fungal keratitis (FK) infections that cause cornea inflammations are more virulent than other bacterial keratitis infections and remain one of the most ethereal and challenging infections for ophthalmologists to diagnose and treat. Thus, the urgency in understanding the current perspectives of antifungal agents and their interactions with novel therapeutic targets and the identification of novel anti-fungal agents are at the frontline of studies in the pharmaceutical industry. In this study, DNA dependent RNA polymerase was modelled and virtually screened against eight antifungal agents, and it was found that Itraconazole (−22.0427 kJ/mol), Ketoconazole (−20.2194 kJ/mol), and Voriconazole (−12.6388 kJ/mol) exhibited better binding interactions. further, the structural and electronic properties of Itraconazole calculated through density functional theory studies revealed the sites of chemical reactivity that are vital in the compounds for possible interactions with RNA polymerase (RNAP). Hence, this study explores the binding efficacies of various anti-fungal agents through docking studies and their chemical entities, which might pave a significant path for the design of novel anti-fungal agents against hyalohyphomycetes causing keratitis.