Drug-drug Combinations Research Articles

Abstract P240: Benchmarking the novel dual-MEK inhibitor, IMM-1-104, head-to-head and in combination with sotorasib (AMG-510) in the MIA PaCa-2 (KRAS-G12C) pancreatic cancer xenograft model

Abstract Background: Approximately 9 out of 10 patients diagnosed with pancreatic cancer have tumors driven by a single activating mutation in KRAS. These tumors remain unaddressable by current therapies and collectively represent a high unmet clinical need. KRAS is the most commonly mutated form of RAS and represents approximately 85% of all RAS mutant human tumors with HRAS and NRAS accounting for the remaining 15%. Sotorasib (AMG-510), a covalent KRAS-G12C inhibitor, recently received accelerated regulatory approval for KRAS-G12C lung cancer in the United States. While the G12C mutation has been documented in up to 15% of all KRAS mutant tumors, it is rarely observed (<1%) in human pancreatic cancer, which could severely limit this type of new inhibitor in certain histologies. IMM-1-104 is a novel dual-MEK inhibitor that resists CRAF-bypass (goal: improve activity against RAS mutant tumors), and is designed to have a short plasma drug half-life, which leads to deep, cyclic inhibition of the MAPK pathway (goal: improved tolerability). Materials & Methods: The novel, orally bioavailable dual-MEK inhibitor, IMM-1-104, was evaluated for antitumor activity, as a single agent (50, 100, 150 mg/kg BID p.o.) in a head-to-head format against sotorasib (10, 30, 100 mg/kg QD p.o.) or in combination (50, 100, 150 mg/kg BID p.o. IMM-1-104 with 30 mg/kg QD p.o. sotorasib) in the MIA PaCa-2 KRAS-G12C mutant pancreatic tumor xenograft model. Tumor-bearing animals were treated for 21 days of oral dosing, before drug treatments were terminated and animals monitored for additional 3 weeks. Results: IMM-1-104 and sotorasib were well-tolerated as single agents and in combination with each other across all dose levels tested. Both drugs demonstrated dose-dependent antitumor activity with tumor regressions noted at higher dose levels. IMM-1-104 in combination with sotorasib produced deep regressions that were sustained longer than either drug alone. Conclusions: The dual-MEK mechanism and short half-life of IMM-1-104 has demonstrated broad activity with low toxicity across multiple RAS and RAF mutant preclinical tumor models. Tumor regressions were observed in NRAS mutant melanoma (SK-MEL-2) and KRAS mutant colorectal (Colon-26) rodent tumor models, and tumor stasis was observed in KRAS mutant lung (A549) and BRAF mutant melanoma (A375) tumor xenograft models. Here, we report that IMM-1-104 treatment resulted in tumor regressions similar to that observed for sotorasib in the recently benchmarked KRAS-G12C mutant pancreatic cancer xenograft model (MIA PaCa-2). IMM-1-104 in combination with sotorasib promoted deep, durable tumor regressions, when compared to either drug alone. Therefore, future drug-drug combinations with upstream inhibitors such as sotorasib may afford greater durability in combination for patients with KRAS-G12C and other select tumor types. In totality, these data suggest the potential for broad, single agent activity of IMM-1-104 in tumors with inappropriately elevated MAPK signaling, including a large percentage of KRAS mutant pancreatic cancer. Citation Format: Peter J. King, Amy E. Axel, Kevin D. Fowler, Sarah E. Kolitz, Scott Barrett, Benjamin J. Zeskind, Brett M. Hall. Benchmarking the novel dual-MEK inhibitor, IMM-1-104, head-to-head and in combination with sotorasib (AMG-510) in the MIA PaCa-2 (KRAS-G12C) pancreatic cancer xenograft model [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P240.

Abstract 3055: Clonogenic high-throughput assay for screening radiation modulators

Abstract We present here the development of a high-throughput clonogenic methodology for the identification of novel radiation modulators, with critical and laborious steps automated for speed and ease of use. Clonogenic assays have long been the benchmark methodology for assessing reproductive cell death following treatment with ionizing radiation, alone or in combination with a variety of chemotherapeutic agents (cytotoxic, genotoxic, targeted therapies). However, owning to the large growth formats and intrinsic laborious nature of clonogenic assays heretofore large-scale screening for radiation modulators with this assay has been impractical. Further, presently available large-scale cellular growth screens (e.g. MTT type assays) have proven unreliable at reproducibly identifying clinically relevant radiation modulators. As a result, numerous attempts have been made at developing a streamlined clonogenic process, with modest degrees of success. Here we present the development of an automated miniaturized clonogenic methodology that finally provides for the high throughput screening of novel radiation modulators in cell lines representing seven cancer indications where radiation therapy is the standard of care (Esophageal cancer, Glioblastoma, Head and Neck cancer, metastatic Melanoma, Non-small-cell lung carcinoma, Rectal cancer, and Soft tissue sarcoma). First, we have identified multiple cell lines for each cancer indication that form consistently small and discrete colonies, making them amenable to clonogenic growth in 24- and/or 96-well formats. Second, we have identified conditions in which chemoradiation response is identical between traditional and miniaturized clonogenic formats. Third, we have developed fluorescent cell labeling protocols that allow for live cell colony counting, streamlining the assay and allowing for real-time monitoring over the course of each experiment. Fourth, we have developed software for the automated calling/quantification of clonogenic images, calculation of α/β ratio, dose modifying factor, and calling of hits in large scale screening. Fifth, we have developed an instrument that in large scale is capable of robotic liquid handling, irradiation, drug treatment, and incubation of clonogenic plates in an integrated manner. Preliminary screens with this methodology have demonstrated its ability to accurately identify known radiation modulators. Taken together these new technologies provide a platform to allow for the high-throughput clonogenic screening of drug libraries to identify novel radiation modulators. Further, this methodology is adaptable for identification of novel drug-drug combinations using a clonogenic endpoint. This work is funded in part by NCI SBIR Phase II contract No. 75N9109C0038. Citation Format: Barbara Frederick, Nathan Gomes, Tin Tin Su. Clonogenic high-throughput assay for screening radiation modulators [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 3055.

Abstract PO-080: Clonogenic high-throughput assay for screening radiation modulators

Abstract We present here the development of a high-throughput clonogenic methodology for the identification of novel radiation modulators, with critical and laborious steps automated for speed and ease of use. Clonogenic assays have long been the benchmark methodology for assessing reproductive cell death following treatment with ionizing radiation, alone or in combination with a variety of chemotherapeutic agents (cytotoxic, genotoxic, targeted therapies). However, owning to the large growth formats and intrinsic laborious nature of clonogenic assays heretofore large-scale screening for radiation modulators with this assay has been impractical. Further, presently available large-scale cellular growth screens (e.g. MTT type assays) have proven unreliable at reproducibly identifying clinically relevant radiation modulators. As a result, numerous attempts have been made at developing a streamlined clonogenic process, with modest degrees of success. Here we present the development of an automated miniaturized clonogenic methodology that finally provides for the high throughput screening of novel radiation modulators in cell lines representing seven cancer indications where radiation therapy is the standard of care (Esophageal cancer, Glioblastoma, Head and Neck cancer, metastatic Melanoma, Non-small-cell lung carcinoma, Rectal cancer, and Soft tissue sarcoma). First, we have identified multiple cell lines for each cancer indication that form consistently small and discrete colonies, making them amenable to clonogenic growth in 24- and/or 96-well formats. Second, we have identified conditions in which chemoradiation response is identical between traditional and miniaturized clonogenic formats. Third, we have developed fluorescent cell labeling protocols that allow for live cell colony counting, streamlining the assay and allowing for real-time monitoring over the course of each experiment. Fourth, we have developed software for the automated calling/quantification of clonogenic images, calculation of α/β ratio, dose modifying factor, and calling of hits in large scale screening. Fifth, we have developed an instrument that in large scale is capable of robotic liquid handling, irradiation, drug treatment, and incubation of clonogenic plates in an integrated manner. Preliminary screens with this methodology have demonstrated its ability to accurately identify known radiation modulators. Taken together these new technologies provide a platform to allow for the high-throughput clonogenic screening of drug libraries to identify novel radiation modulators. Further, this methodology is adaptable for identification of novel drug-drug combinations using a clonogenic endpoint. This work is funded in part by NCI SBIR Phase II contract No. 75N9109C0038. Citation Format: Barbara Frederick, Nathan Gomes, Tin Tin Su. Clonogenic high-throughput assay for screening radiation modulators [abstract]. In: Proceedings of the AACR Virtual Special Conference on Radiation Science and Medicine; 2021 Mar 2-3. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(8_Suppl):Abstract nr PO-080.

AI – New Avenue for Drug Discovery and Optimization

The artificial intelligence (AI) used in drug treatment have to do with matching patients to their predicting drug-target or drug-drug interactions, optimal drug or combination of drugs, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the suitable drug for a patient typically requires the patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The forecast of drug relations often relies on similarity metrics, pretentious that drugs with similar structures or targeted and similar behaviour or may interfere with each other. Deciding the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamics data.

Inhibition of drug-metabolizing enzymes by Qingfei Paidu decoction: Implication of herb-drug interactions in COVID-19 pharmacotherapy

Corona Virus Disease 2019 (COVID-19) has spread all over the world and brings significantly negative effects on human health. To fight against COVID-19 in a more efficient way, drug-drug or drug-herb combinations are frequently used in clinical settings. The concomitant use of multiple medications may trigger clinically relevant drug/herb-drug interactions. This study aims to assay the inhibitory potentials of Qingfei Paidu decoction (QPD, a Chinese medicine compound formula recommended for combating COVID-19 in China) against human drug-metabolizing enzymes and to assess the pharmacokinetic interactions in vivo. The results demonstrated that QPD dose-dependently inhibited CYPs1A, 2A6, 2C8, 2C9, 2C19, 2D6 and 2E1 but inhibited CYP3A in a time- and NADPH-dependent manner. In vivo test showed that QPD prolonged the half-life of lopinavir (a CYP3A substrate-drug) by 1.40-fold and increased the AUC of lopinavir by 2.04-fold, when QPD (6 g/kg) was co-administrated with lopinavir (160 mg/kg) to rats. Further investigation revealed that Fructus Aurantii Immaturus (Zhishi) in QPD caused significant loss of CYP3A activity in NADPH-generating system. Collectively, our findings revealed that QPD potently inactivated CYP3A and significantly modulated the pharmacokinetics of CYP3A substrate-drugs, which would be very helpful for the patients and clinicians to avoid potential drug-interaction risks in COVID-19 treatment.

Identifying and Characterizing Serious Adverse Drug Reactions Associated With Drug-Drug Interactions in a Spontaneous Reporting Database.

Background: Drug-drug interactions (DDIs) are an important cause of adverse drug reactions (ADRs). In literature most of studies focus only on potential DDIs, while detailed data on serious ADRs associated with DDIs are limited. Our aim is to identify and characterize serious ADRs caused by DDIs using a spontaneous reporting database. Methods: All serious ADR reports, not related to vaccines and with a “definite”, “probable” or “possible” causality assessment, inserted into the National Pharmacovigilance database from Veneto Region (January 1, 2015 to May 31, 2020) were analyzed. A list of drug pairs was created by selecting the reports containing at least two suspected or concomitant drugs. We verified which drug pairs potentially interacted according to the online version of DRUGDEX® system. For each potential DDI we controlled whether the ADR description in the report corresponded to the interaction effect as described in Micromedex. A detailed characterization of all serious reports containing an occurring DDI was performed. Results: In the study period a total of 31,604 reports of suspected ADRs from the Veneto Region were identified, of which 2,195 serious reports (6.9% of all ADR reports) containing at least two suspected or concomitant drugs were analyzed. We identified 1,208 ADR reports with at least one potential DDI (55.0% of 2,195) and 381 reports (17.4% of 2,195 reports) with an occurring ADR associated with a DDI. The median age of patients and the number of contraindicated or major DDIs were significantly higher in reports with an occurring DDI. Warfarin was the most frequently reported interacting drug and the most common ADRs were gastrointestinal or cerebral hemorrhagic events. The proton pump inhibitors/warfarin, followed by platelet aggregation inhibitors/warfarin were the drug-drug combinations most frequently involved in ADRs caused by DDIs. The highest proportion of fatal reports was observed with platelet aggregation inhibitors/warfarin and antidepressants/warfarin. Conclusion: Our findings showed that about one-third of patients exposed to a potential DDI actually experienced a serious ADR. Furthermore, our study confirms that a spontaneous reporting database could be a valuable resource for identifying and characterizing ADRs caused by DDIs and the drugs leading to serious ADRs and deaths.

New and simplified method for drug combination studies by checkerboard assay

To evaluate the effect of two combined antimicrobial drugs, one method currently in use is the checkerboard assay in a 96-well microplate, which gives a good in vivo estimation of the drug-drug combination effect. Appropriate and consolidated methods are described in numerous scientific publications which are, however, in turn, laborious and time-spending, specifically for the setting of the 96-well microplate preparation. Each drug of every combination must be prepared and dispensed individually in several steps, often limiting its use in terms of consumed materials and working time. In our method, the strengths of the previous consolidated techniques are kept, although the toughness and the execution time are drastically reduced. No special laboratory apparatuses are needed. All the procedures of our method can be referred to the CLSI or EUCAST guideline. The method provides few main steps, which can be summarised in:•Preparation of the microorganism inoculum and three concentrations of antimicrobial drugs.•Easy dispensing of all reagents into the microplates with a multichannel pipette.•Evaluation of the microorganism optical density (OD) by a microplate reader, and calculation of growth percentage for each of the 77 combinations.

EFFECT OF OLMESARTAN ON PHARMACODYNAMICS AND PHARMACOKINETICS OF GLIMEPIRIDE AND METFORMIN COMBINATION IN ANIMAL MODELS

To establish the safety and efficacy of drug-drug combinations in diabetic hypertensive patients, the present study was designed to identify the pharmacodynamic and pharmacokinetic influence of olmesartan on glimepiride + metformin combination in normal rats, diabetic rats and normal rabbits using treatment of single and multiple doses. Blood samples were analyzed for blood glucose by GOD/POD method, insulin by elISA and serum glimepiride levels by HPlC method at pre-determined time intervals. Olmesartan showed significant synergistic hypoglycemic activity with glimepiride + metformin as well as elevated serum insulin levels in single and multiple-dose combinations in both rats and rabbits. The pharmacokinetic interactions of this combination are evidenced by a significant increase in AUMC0-t, AUMC0-∞ and MRT and there is a decrease in clearance (Cl) of glimepiride + metformin with single and multiple-dose treatment of olmesartan. Hence, care must be taken by physicians while prescribing olmesartan and glimepiride + metformin combination in a clinical situation.

Assessment of Intravenous Drugs Admixtures Incompatibilities at Nekemte Referral Hospital, West Ethiopia

Intravenous admixtures are the preparations consisting of sterile drug products added to an IV fluid (s) for medication purposes. Especial concerns are required for such drugs since the reversal of toxicity and/or unwanted effects following incompatibilities are cumbersome to deal with. Concern drugs that may cause irritation or toxicity when given as a rapid direct IV injection are also prepared as IV admixtures. The ultimate goal of this assessment was to figure out the actual practices on the ground in IV medications administered in NRH with IV fluids emphasizing on incidence and prevalence of incompatibility between drugs themselves or with IV fluids. This study was a cross-sectional prospective observational study which investigated the potential incompatibilities associated with IV admixture, & the incidence of IV drug administration errors using available evidence-based medicine sources at Nekemte Referral Hospital in West Ethiopia. The data was collected from patient medication Charts and prescription papers for patients treated within a period of 2 months. The study period was from February 2017 to April 2017 at the medical and pediatrics wards of NRH. About patients using IV admixtures were followed within this study period. The prevalence of identified incompatibility among IV admixtures ingredients was 28.94%. The identified compatibility was only 25.43%, while 38.59%were undocumented combinations, and 7.01% were variable. The most commonly encountered incompatible drug-solute combinations were Ringer lactate (61.8%), Insulin + DNS (29.4) and Ceftriaxone + Phenytoin + 0.9% NaCl (8.8%). Also, the incompatible drug-drug combinations detected in this study were Ceftriaxone+ heparin and clopidgrel+cimetidine combinations. Based on the findings of this study, the following can be concluded; there is a high prevalence of incompatibilities among IV admixtures. The types of incompatibilities were both drug-solute and drug-drug incompatibilities. Prescribers and administrators of IV admixtures were recommended to check plausibility before administration to prevent such incompatibilities which can compromise the treatment outcome of patients.

Deep graph embedding for prioritizing synergistic anticancer drug combinations

Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graphembedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein–protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico.

Drug-Drug Combinations Can Enhance Toxicity as Shown by Monocyte-Derived Hepatocyte-like Cells From Patients With Idiosyncratic Drug-Induced LiverInjury.

ClinicalTrials.gov NCT02353455.

Abstract 4245: Drug response metrics and pharmacological profiling using the OncoPanel™ cell-based profiling service

Abstract High-throughput, cell-based drug screening platforms are essential to guiding drug discovery programs and can rapidly generate large amounts of pharmacological information about a candidate drug. Drug response data combined with genomic and phenotypic information enables an exploratory analysis that may reveal potential associative biomarkers and deepen the pharmacological profile of a drug. The choice of metric is an important consideration as different metrics will reflect different underlying biological mechanisms. Normalized growth rate inhibition (GR) metrics offer an alternative summary of drug response, and may eliminate bias associated with experimental factors that contribute to variation in growth rate. In these sets of experiments, we used drug response data, generated with the Eurofins OncoPanel cell-based profiling service, to investigate a variety of standard of care compounds against a cell line panel selected for variation in growth rate, cancer indication, cancer hallmark mutational status, and baseline apoptosis rate. Seeding density and incubation times were systematically varied to investigate the relationship between end-point confluence, kinetics, genotype, apoptotic priming, and the robustness of different dose response metrics. Based on these results, select cell lines and standard of care compounds were included in a series of drug-drug combination studies to further investigate the biological underpinning of synergy, at the same time exploring how alternative metrics influence pharmacological profiling experiments. Compounds were added to cells in 10 half-log dilutions, in triplicate, and allowed to incubate for 1, 3, 5, or 10 days. Initial cell density conditions were varied to allow for a final targeted confluence of 30, 80, or 100%. A time-zero plate was fixed at compound addition to allow for GR calculations. Following incubation, the cells were fixed, stained with DAPI, and imaged with a high-content imaging system. Cell proliferation dose response curves were fitted using a 4-parameter log-logistic model with custom curve-fitting software. These data were re-analyzed using the GRmetrics package in R to determine the GR metrics. Summary parameters, including IC50, GR50, and GRmax, were compared in subsequent analyses. For the combination analyses, a 9 x 9 concentration matrix design was used, and Bliss analysis was conducted to assess synergy. Single parameters are typically used to summarize drug responses in cell lines, which are used to classify cell lines as sensitive or resistant to the drug, impacting the interpretation of downstream analyses. We examined the robustness of different parameters, under various growth conditions for select standard of care compounds against a panel of cancer cell lines, and investigated their interpretation on biomarker and drug-drug combination analyses. Citation Format: Charles R. Wageman, Lee R. Cavedine, Vanessa Norman, Natiya Robinson, Tracy Lu, Victoria McBain, Joseph Murphy, Kayla Stehle, Steven M. Garner, Alyssa M. Croff, Brogan A. Epkins, Kristin Dempsey, Alastair J. King, Jesse J. Parry. Drug response metrics and pharmacological profiling using the OncoPanel™ cell-based profiling service [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4245.

Abstract 5195: Efficacy of histone deacetylase inhibitors in a 3D cell culture model

Abstract Epigenetic dysregulation, changes in gene expression not due to changes in the DNA sequence, is the underlying cause of many diseases including cancer. Identification of enzymatic chromatin-complex modulators that can restore chromatin homeostasis is critical to developing better therapeutic agents for both, hematologic and solid tumors. 3D cell culture models have been developed to overcome the limitations of monolayer in vitro assays, to include the complexity of the tumor microenvironment known to modulate epigenetic factors, such as cell-cell interaction and allowing for formations of metabolic gradients, factors known to play a role in epigenetic drug efficacy and resistance. In this study, we evaluated the efficacy of class I and II Inhibitors of histone deacetylases, SAHA and Trichostatin A, in a 3D cell culture in vitro assay across several human tumor carcinoma cell lines. The cell lines were grown in 3D cell cultures, and efficacy of the drugs was evaluated at 72 hours and ten days. All the cell lines grown as spheroids proved to be highly sensitive to SAHA and Trichostation A. AGS, a stomach adenocarcinoma cell line, showed the highest sensitivity to SAHA with an IC50 of 270 nM. Trichostatin A, on the other hand, displayed higher sensitivity to the ovarian carcinoma cell line, SKOV3, with an IC50 of 190 nM. In all cases, maximum potency was seen at the 10-day incubations with IC50s below 1 µM for all the cell lines in the panel. Drug-drug combinations with the epigenetic drugs were also studied, the results are discussed. Citation Format: Macarena Irigoyen, Gonzalo Castillo. Efficacy of histone deacetylase inhibitors in a 3D cell culture model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5195.

Systematic analysis of drug combinations that mitigate adverse drug reactions

Seeking beneficial drug–drug combinations (DDCs) from real-world evidence is an emerging topic in phenotypic drug discovery. With sophisticated algorithms, the numbers of DDC hypotheses generated often reach to tens of thousands. However, due to limited resources, only a few, top-ranking hypotheses are selected for experimental validations. Often, researchers start from the topmost DDC pairs and work their way down until they find a pair with successful validation. While this is an established way of performing validations, there still exists room to improve. Here, we present a systematic approach to perform a secondary analysis on the DDCs to streamline the validation procedure. Specifically, we propose a method in which we search for additional patterns in terms of chemical classes and biological target interactions of the drug pair. Using 78,345 DDC hypotheses generated from the FDA Adverse Event Reporting System (FAERS) data in our prior study, we demonstrate how the proposed analysis can reveal additional biochemical and mechanical insights of drug interactions that can streamline experimental validation.

Análisis de los problemas relacionados con los medicamentos en un hospital de tercer nivel de Barcelona

ObjectiveTo describe drug-related problems identified in hospitalized patients and to assess physicians’ acceptance rate of pharmacists’ recommendations. MethodsRetrospective observational study that included all drug-related problems detected in hospitalized patients during 2014-2015. Statistical analysis included a descriptive analysis of the data and a multivariate logistic regression to evaluate the association between pharmacists’ recommendation acceptance rate and the variable of interest. ResultsDuring the study period 4587 drug-related problems were identified in 44,870 hospitalized patients. Main drug-related problems were prescription errors due to incorrect use of the computerized physician order entry (18.1%), inappropriate drug-drug combination (13.3%) and dose adjustment by renal and/or hepatic function (11.5%). Acceptance rate of pharmacist therapy advice in evaluable cases was 81.0%. Medical versus surgical admitting department, specific types of intervention (addition of a new drug, drug discontinuation and correction of a prescription error) and oral communication of the recommendation were associated with a higher acceptance rate. ConclusionsThe results of this study allow areas to be identified on which to implement optimization strategies. These include training courses for physicians on the computerized physician order entry, on drugs that need dose adjustment with renal impairment, and on relevant drug interactions.

Pentoxifylline, dexamethasone and azithromycin demonstrate distinct age-dependent and synergistic inhibition of TLR- and inflammasome-mediated cytokine production in human newborn and adult blood in vitro.

IntroductionNeonatal inflammation, mediated in part through Toll-like receptor (TLR) and inflammasome signaling, contributes to adverse outcomes including organ injury. Pentoxifylline (PTX), a phosphodiesterase inhibitor which potently suppresses cytokine production in newborn cord blood, is a candidate neonatal anti-inflammatory agent. We hypothesized that combinations of PTX with other anti-inflammatory agents, the steroid dexamethasone (DEX) or the macrolide azithromycin (AZI), may exert broader, more profound and/or synergistic anti-inflammatory activity towards neonatal TLR- and inflammasome-mediated cytokine production.MethodsWhole newborn and adult blood was treated with PTX (50–200 μM), DEX (10−10–10−7 M), or AZI (2.5–20 μM), alone or combined, and cultured with lipopolysaccharide (LPS) (TLR4 agonist), R848 (TLR7/8 agonist) or LPS/adenosine triphosphate (ATP) (inflammasome induction). Supernatant and intracellular cytokines, signaling molecules and mRNA were measured by multiplex assay, flow cytometry and real-time PCR. Drug interactions were assessed based on Loewe's additivity.ResultsPTX, DEX and AZI inhibited TLR- and/or inflammasome-mediated cytokine production in newborn and adult blood, whether added before, simultaneously or after TLR stimulation. PTX preferentially inhibited pro-inflammatory cytokines especially TNF. DEX inhibited IL-10 in newborn, and TNF, IL-1β, IL-6 and interferon-α in newborn and adult blood. AZI inhibited R848-induced TNF, IL-1β, IL-6 and IL-10, and LPS-induced IL-1β and IL-10. (PTX+DEX) synergistically decreased LPS- and LPS/ATP-induced TNF, IL-1β, and IL-6, and R848-induced IL-1β and interferon-α, while (PTX+AZI) synergistically decreased induction of TNF, IL-1β, and IL-6. Synergistic inhibition of TNF production by (PTX+DEX) was especially pronounced in newborn vs. adult blood and was accompanied by reduction of TNF mRNA and enhancement of IL10 mRNA.ConclusionsAge, agent, and specific drug-drug combinations exert distinct anti-inflammatory effects towards TLR- and/or inflammasome-mediated cytokine production in human newborn blood in vitro. Synergistic combinations of PTX, DEX and AZI may offer benefit for prevention and/or treatment of neonatal inflammatory conditions while potentially limiting drug exposure and toxicity.

Importance of integrating nanotechnology with pharmacology and physiology for innovative drug delivery and therapy - an illustration with firsthand examples.

Nanotechnology has been applied extensively in drug delivery to improve the therapeutic outcomes of various diseases. Tremendous efforts have been focused on the development of novel nanoparticles and delineation of the physicochemical properties of nanoparticles in relation to their biological fate and functions. However, in the design and evaluation of these nanotechnology-based drug delivery systems, the pharmacology of delivered drugs and the (patho-)physiology of the host have received less attention. In this review, we discuss important pharmacological mechanisms, physiological characteristics, and pathological factors that have been integrated into the design of nanotechnology-enabled drug delivery systems and therapies. Firsthand examples are presented to illustrate the principles and advantages of such integrative design strategies for cancer treatment by exploiting 1) intracellular synergistic interactions of drug-drug and drug-nanomaterial combinations to overcome multidrug-resistant cancer, 2) the blood flow direction of the circulatory system to maximize drug delivery to the tumor neovasculature and cells overexpressing integrin receptors for lung metastases, 3) endogenous lipoproteins to decorate nanocarriers and transport them across the blood-brain barrier for brain metastases, and 4) distinct pathological factors in the tumor microenvironment to develop pH- and oxidative stress-responsive hybrid manganese dioxide nanoparticles for enhanced radiotherapy. Regarding the application in diabetes management, a nanotechnology-enabled closed-loop insulin delivery system was devised to provide dynamic insulin release at a physiologically relevant time scale and glucose levels. These examples, together with other research results, suggest that utilization of the interplay of pharmacology, (patho-)physiology and nanotechnology is a facile approach to develop innovative drug delivery systems and therapies with high efficiency and translational potential.

MSBIS: A Multi-Step Biomedical Informatics Screening Approach for Identifying Medications that Mitigate the Risks of Metoclopramide-Induced Tardive Dyskinesia

In 2009 the U.S. Food and Drug Administration (FDA) placed a black box warning on metoclopramide (MCP) due to the increased risks and prevalence of tardive dyskinesia (TD). In this study, we developed a multi-step biomedical informatics screening (MSBIS) approach leveraging publicly available bioactivity and drug safety data to identify concomitant drugs that mitigate the risks of MCP-induced TD. MSBIS includes (1) TargetSearch (http://dxulab.org/software) bioinformatics scoring for drug anticholinergic activity using CHEMBL bioactivity data; (2) unadjusted odds ratio (UOR) scoring for indications of TD-mitigating effects using the FDA Adverse Event Reporting System (FAERS); (3) adjusted odds ratio (AOR) re-scoring by removing the effect of cofounding factors (age, gender, reporting year); (4) logistic regression (LR) coefficient scoring for confirming the best TD-mitigating drug candidates. Drugs with increasing TD protective potential and statistical significance were obtained at each screening step. Fentanyl is identified as the most promising drug against MCP-induced TD (coefficient: −2.68; p-value<0.01). The discovery is supported by clinical reports that patients fully recovered from MCP-induced TD after fentanyl-induced general anesthesia. Loperamide is identified as a potent mitigating drug against a broader range of drug-induced movement disorders through pharmacokinetic modifications. Using drug-induced TD as an example, we demonstrated that MSBIS is an efficient in silico tool for unknown drug-drug interaction detection, drug repurposing, and combination therapy design.

Abstract 5767: Ex vivo three-dimensional tumor growth assay: 3DX-TGA

Abstract With a 7% likelihood of regulatory approval, oncology drug registration failure rates lead all therapeutic areas. Further complicating the drug development process, preclinical oncology models poorly reflect tumor tissue biology and produce a high level of false positive data. Yet, monocellular two-dimensional (2D) tissue culture remains the preferred platform for most laboratory preclinical studies. The popularity of 2D tissue culture was driven predominantly by fast and dependable proliferation of human tumor cells rather than alignment to the pathophysiology of human cancer. As a consequence of this artificial selection bias, many registered oncology drugs today are highly toxic with a propensity to attack any dividing cell (i.e., non-targeted). Fortunately, recent advances in genomics, metabolomics and immunology have aided in development of new targeted agents, many of which have achieved regulatory approval. Nevertheless, rational drug combinations and identification of clinically meaningful drug targets persist as major challenges in oncology, and each can be meaningfully addressed using more diverse and biologically relevant preclinical models. Ex vivo tumor tissue transplant models, such as patient-derived xenografts (PDX) offer greater model diversity and more faithfully reflect patient tumor genetics. However, cost and scalability barriers tend to limit widespread and practical model utility. In addition, there remains a strong selection bias for autocrine human tumors that can quickly adapt to a cross-species mouse host. As a practical alternative to PDX models, we established, serially propagated and molecularly characterized 300 ex vivo 3D (3DX) models spanning 15 tumor indications. Given patient tumor seeding success rates of over 95%, the 3DX-TGA model enabled diverse pharmacologic evaluation for the vast majority of cancer patient models attempted. After achieving sufficient tumor biomass, typically within 7 to 8 weeks, thirty-one drugs were screened for proliferation and viability endpoints over a three-log dose range. Resistance and sensitivity profiles mirrored population-based response rates for indication-aligned FDA-registered drugs. Taken together, the 3DX-TGA model represents a powerful preclinical ex vivo model that: (1.) more faithfully recapitulates human tumor biology, (2.) can be scaled at a fraction of time and cost compared to PDX models, and (3.) provide a superior screening platform for novel drugs and drug-drug combinations. Citation Format: Praveen Nair, Dileep Nair, Kaede Hinata, Cyrus Mirsaidi, Junjie Wu, Yong Hu, Brett M. Hall. Ex vivo three-dimensional tumor growth assay: 3DX-TGA [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 5767. doi:10.1158/1538-7445.AM2017-5767

Abstract 2991: Integrative analysis of molecular and drug response data from clinical samples and PDTXs to identify pharmacogenomic associations in breast cancer

Abstract Breast cancer is the most common cancer among women showing high clinical and molecular heterogeneity. Current clinical management causes patients overtreatment with implications on both patients’ quality of life and healthcare costs. Moreover, intrinsic or acquired tumor resistance to treatment leads to incurable metastatic progression in a significant proportion of patients. Consequently, there is an urgent need for better predictive biomarkers and a better understanding of the mechanisms driving response to treatment. As part of the METABRIC initiative, we fully molecularly characterized 2000 breast primary tumors, measuring gene expression, copy number aberration, somatic mutations and methylation. In addition, a biobank of breast cancer patient-derived tumor xenograft (PDTX) models (n=92) has been generated in our lab and a comprehensive molecular characterization was also obtained. We recently demonstrated that breast cancer PDTXs maintain originating cancers intra-tumor heterogeneity, hence representing a more relevant preclinical model than cell lines. An ex-vivo drug screening was performed in these models generating response data (IC50 and AUC) for 100 different drugs, including "best in class" PI3K, PARP and CDK4/6 inhibitors, novel biological and chemical inhibitors of HER2, ER, IGF1R and HER3, as well as standard of care agents. Here we derived signatures of pathway activation/disruption by integrating different data types in the METABRIC cohort. Their association with previous breast cancer classifications, as well as their prognostic significance was studied. The predictive power of these signatures was investigated in the PDTX cohort to identify novel pharmacogenomics associations. They were tested independently as well as in combination, to derive molecular predictors of response to treatment. We found known and novel associations between genomic/transcriptomic features and drug response. For example, our results confirmed the known association between estrogen receptor-related genes and response to tamoxifen treatment. We also identified markers of response to inhibitors of the PI3K/AKT/mTOR pathway. Selected findings were validated in clinical cohorts as well as in independent PDTX models. In conclusion, by integrating molecular data from large cohorts of clinical samples and PDTX we have generated a computational framework for the systematic identification of pharmacogenomics associations in breast cancer and to generate hypothesis for rational drug-drug combinations. Citation Format: Maurizio Callari, Rajbir N. Batra, Ankita Sati Batra, Wendy Greenwood, Suet-Feung Chin, Alejandra Bruna, Oscar M. Rueda, Carlos Caldas. Integrative analysis of molecular and drug response data from clinical samples and PDTXs to identify pharmacogenomic associations in breast cancer [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 2991. doi:10.1158/1538-7445.AM2017-2991