Predict Drug Efficacy Research Articles

Context-specific functional module based drug efficacy prediction.

BackgroundIt is necessary to evaluate the efficacy of individual drugs on patients to realize personalized medicine. Testing drugs on patients in clinical trial is the only way to evaluate the efficacy of drugs. The approach is labour intensive and requires overwhelming costs and a number of experiments. Therefore, preclinical model system has been intensively investigated for predicting the efficacy of drugs. Current computational drug sensitivity prediction approaches use general biological network modules as their prediction features. Therefore, they miss indirect effectors or the effects from tissue-specific interactions.ResultsWe developed cell line specific functional modules. Enriched scores of functional modules are utilized as cell line specific features to predict the efficacy of drugs. Cell line specific functional modules are clusters of genes, which have similar biological functions in cell line specific networks. We used linear regression for drug efficacy prediction. We assessed the prediction performance in leave-one-out cross-validation (LOOCV). Our method was compared with elastic net model, which is a popular model for drug efficacy prediction. In addition, we analysed drug sensitivity-associated functions of five drugs - lapatinib, erlotinib, raloxifene, tamoxifen and gefitinib- by our model.ConclusionsOur model can provide cell line specific drug efficacy prediction and also provide functions which are associated with drug sensitivity. Therefore, we could utilize drug sensitivity associated functions for drug repositioning or for suggesting secondary drugs for overcoming drug resistance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1078-6) contains supplementary material, which is available to authorized users.

G-Protein/β-Arrestin-Linked Fluctuating Network of G-Protein-Coupled Receptors for Predicting Drug Efficacy and Bias Using Short-Term Molecular Dynamics Simulation

The efficacy and bias of signal transduction induced by a drug at a target protein are closely associated with the benefits and side effects of the drug. In particular, partial agonist activity and G-protein/β-arrestin-biased agonist activity for the G-protein-coupled receptor (GPCR) family, the family with the most target proteins of launched drugs, are key issues in drug discovery. However, designing GPCR drugs with appropriate efficacy and bias is challenging because the dynamic mechanism of signal transduction induced by ligand—receptor interactions is complicated. Here, we identified the G-protein/β-arrestin-linked fluctuating network, which initiates large-scale conformational changes, using sub-microsecond molecular dynamics (MD) simulations of the β2-adrenergic receptor (β2AR) with a diverse collection of ligands and correlation analysis of their G protein/β-arrestin efficacy. The G-protein-linked fluctuating network extends from the ligand-binding site to the G-protein-binding site through the connector region, and the β-arrestin-linked fluctuating network consists of the NPxxY motif and adjacent regions. We confirmed that the averaged values of fluctuation in the fluctuating network detected are good quantitative indexes for explaining G protein/β-arrestin efficacy. These results indicate that short-term MD simulation is a practical method to predict the efficacy and bias of any compound for GPCRs.

Three-dimensional perfused tumour spheroid model for anti-cancer drug screening.

ObjectiveTo build an in vitro-perfused, three-dimensional (3D) spheroid model based on the TissueFlex system for anti-cancer drug efficacy testing in order to mimic avascular micro-tissues with inherent O2, nutrient and metabolite gradients, and to provide a more accurate prediction of drug toxicity and efficacy than traditional in vitro tumour models in conventional static culture well plates.ResultsThe perfused cancer spheroid model showed higher cell viability and increased diameter of spheroids over a relatively long culture period (17 days). Three anti-cancer drugs with different cytotoxic mechanisms were tested. In perfusion, lower cytotoxicity was observed for traditional cytotoxic drug 5-fluorouracil and microtubule-interfering, paclitaxel, showed greater interruption of spheroid integrity. For the hypoxic-dependent drug, tirapazamine, there was no significant difference observed between static and perfusion cultures.ConclusionThe perfusion culture provides a better homeostasis for cancer cell growth in a more controllable working platform for long-term drug testing.Electronic supplementary materialThe online version of this article (doi:10.1007/s10529-016-2035-1) contains supplementary material, which is available to authorized users.

Visualizing spatial distribution of alectinib in murine brain using quantitative mass spectrometry imaging.

In the development of anticancer drugs, drug concentration measurements in the target tissue have been thought to be crucial for predicting drug efficacy and safety. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is commonly used for determination of average drug concentrations; however, complete loss of spatial information in the target tissue occurs. Mass spectrometry imaging (MSI) has been recently applied as an innovative tool for detection of molecular distribution of pharmacological agents in heterogeneous targets. This study examined the intra-brain transitivity of alectinib, a novel anaplastic lymphoma kinase inhibitor, using a combination of matrix-assisted laser desorption ionization–MSI and LC-MS/MS techniques. We first analyzed the pharmacokinetic profiles in FVB mice and then examined the effect of the multidrug resistance protein-1 (MDR1) using Mdr1a/b knockout mice including quantitative distribution of alectinib in the brain. While no differences were observed between the mice for the plasma alectinib concentrations, diffuse alectinib distributions were found in the brain of the Mdr1a/b knockout versus FVB mice. These results indicate the potential for using quantitative MSI for clarifying drug distribution in the brain on a microscopic level, in addition to suggesting a possible use in designing studies for anticancer drug development and translational research.

Pharmacogenomics and chemotherapy

Genetic factors could alter drug metabolism and activity and could predict drug toxicity and/or efficacy. Several chemotherapy agents are administered in different schedules for the treatment of different cancer histotypes. The most used drug in the treatment of gastro-intestinal, head and neck and breast neoplasms is the 5-fluorouracil (5-FU). Capecitabine is a prodrug of 5-FU. Cisplatin based chemotherapy is administered in the treatment of lung, genitourinary tract, head and neck, occult neoplasms, mesothelioma and melanoma. Taxanes are used in lung, breast, head and neck, genitourinary tract neoplasms and sarcomas. Determination of polymorphisms in metabolizing enzymes before the administration of chemotherapy could offer new strategies for optimizing the treatment of individual patients.

Nab-Paclitaxel Plus S-1 Shows Increased Antitumor Activity in Patient-Derived Pancreatic Cancer Xenograft Mouse Models.

To investigate the antitumor activity of nanoparticle albumin-bound paclitaxel (nab-paclitaxel) plus S-1 in patient-derived pancreatic cancer xenograft mouse models and to explore biomarkers that could predict drug efficacy. Ten patient-derived xenograft models were established. The third-generation tumor-bearing mice were randomized into 4 treatment groups: (1) control; (2) S-1; (3) nab-paclitaxel; (4) S-1 plus nab-paclitaxel. Resected tumors were tested by immunohistochemistry for the expression of thymidylate synthase, orotate phosphoribosyltransferase (OPRT), dihydropyrimidine dehydrogenase (DPD), secreted protein that is acidic and rich in cysteine, human epidermal growth factor receptor 2 (HER2), collagen-1, and CD31. Tumor growth inhibition of the S-1 group, nab-paclitaxel group, and combination group was 69.52%, 86.63%, 103.56%, respectively (P < 0.05). The efficacy of S-1 is better in thymidylate synthase-negative, OPRT-positive, and DPD-negative tumors. The efficacy of nab-paclitaxel is better in HER2-positive tumors. Collagen-1 was decreased and CD31 was increased in tumors treated with nab-paclitaxel and S-1 plus nab-paclitaxel compared with control or S-1. This preclinical study showed that S-1 plus nab-paclitaxel exerted significantly better antitumor activity than S-1 or nab-paclitaxel alone. Thymidylate synthase, OPRT, and DPD were possibly biomarkers of S-1 and HER2 of nab-paclitaxel.

The Warburg effect and drug resistance.

The Warburg effect describes the increased utilization of glycolysis rather than oxidative phosphorylation by tumour cells for their energy requirements under physiological oxygen conditions. This effect has been the basis for much speculation on the survival advantage of tumour cells, tumourigenesis and the microenvironment of tumours. More recently, studies have begun to reveal how the Warburg effect could influence drug efficacy and how our understanding of tumour energetics could be exploited to improve drug development. In particular, evidence is emerging demonstrating how better modelling of the tumour metabolic microenvironment could lead to a better prediction of drug efficacy and the identification of new combination strategies. This review will provide details of the current understanding of the complex interplay between glucose metabolism and pharmacology and discuss opportunities for utilizing the Warburg effect in future drug development.

In silico prediction of drug therapy in catecholaminergic polymorphic ventricular tachycardia.

Key points The mechanism of therapeutic efficacy of flecainide for catecholaminergic polymorphic ventricular tachycardia (CPVT) is unclear.Model predictions suggest that Na+ channel effects are insufficient to explain flecainide efficacy in CPVT.This study represents a first step toward predicting therapeutic mechanisms of drug efficacy in the setting of CPVT and then using these mechanisms to guide modelling and simulation to predict alternative drug therapies. Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited arrhythmia syndrome characterized by fatal ventricular arrhythmias in structurally normal hearts during β‐adrenergic stimulation. Current treatment strategies include β‐blockade, flecainide and ICD implementation – none of which is fully effective and each comes with associated risk. Recently, flecainide has gained considerable interest in CPVT treatment, but its mechanism of action for therapeutic efficacy is unclear. In this study, we performed in silico mutagenesis to construct a CPVT model and then used a computational modelling and simulation approach to make predictions of drug mechanisms and efficacy in the setting of CPVT. Experiments were carried out to validate model results. Our simulations revealed that Na+ channel effects are insufficient to explain flecainide efficacy in CPVT. The pure Na+ channel blocker lidocaine and the antianginal ranolazine were additionally tested and also found to be ineffective. When we tested lower dose combination therapy with flecainide, β‐blockade and CaMKII inhibition, our model predicted superior therapeutic efficacy than with flecainide monotherapy. Simulations indicate a polytherapeutic approach may mitigate side‐effects and proarrhythmic potential plaguing CPVT pharmacological management today. Importantly, our prediction of a novel polytherapy for CPVT was confirmed experimentally. Our simulations suggest that flecainide therapeutic efficacy in CPVT is unlikely to derive from primary interactions with the Na+ channel, and benefit may be gained from an alternative multi‐drug regimen.

Abstract PR16: Transcriptional landscape of drug response guides the design of specific and potent drug combinations

Abstract Characterizing the molecular effects of targeted therapies is an important step towards understanding and predicting drug efficacy in cancer. In this work, we used the L1000 assay developed at the Broad Institute to measure the transcriptional response of six breast cancer cell lines to more than 100 different targeted drugs, many of whom are in clinical trials. We focused on inhibitors targeting the most important signaling kinases such as PI3K, AKT or MAPK, as well as receptor tyrosine kinases (RTKs) and cyclin-dependent kinases (CDKs). With two time points and six doses, the dataset contains more than 8000 unique perturbations. We clustered the perturbations that elicited a significant response (37% of measured perturbations when using p&amp;lt;0.05) according to their gene expression profile and obtained 23 clusters. The perturbation significance was correlated with inhibitor dose, but no cluster was biased toward a particular concentration. Clusters were generally time point specific: the transcriptional responses at 3 hours differed significantly from the 24 hour ones. Some clusters contained perturbations from multiple cell lines whereas others were cell line specific. In particular, responses to CDK inhibitors were similar across most cell lines and showed a down-regulation of genes related to the cell cycle. On the other hand, cell lines responded differently to PI3K/AKT and MAPK inhibitors as illustrated by clusters specific to each cell line and pathway. Interestingly, the perturbations induced by RTK (e.g. EGFR, MET, ALK) and non-RTK (e.g. SRC, ABL, BTK) inhibitors, clustered with either the PI3K/AKT or the MAPK inhibitors depending on the cell line. Thus the transcriptional response allowed us to identify differences in pathway connectivity between cell lines, in particular which RTK connects to the PI3K/AKT pathway or the MAPK one. In parallel to the transcriptional response, we measured the growth inhibition after three days of treatment. We found diverse phenotypic responses that are not necessarily related to the strength of the transcriptional signature. In particular, we identified cases where inhibitors had little effect on growth, yet induced a significant transcriptional response. These cases suggest possible adaptation mechanisms to the inhibitors that may lead to drug resistance. We followed these cases experimentally and identified cell line-specific resistance mechanisms. Finally, we used the directionality of the transcriptional response in the mRNA space to guide co-drugging strategies. By testing if targeting parallel pathways is more potent than inhibiting twice the same pathway, we derived a systematic approach to design synergetic combinations. Because of the differential responses across cell lines, we were able to find combinations that are potent only in a specific cell line. This approach is a step toward the design of co-drugging strategies with differential effect and large therapeutic windows. In conclusion, our measurements of expression signatures and cellular phenotype across thousands of perturbations and six breast cancer cell lines allowed differential analyses that complement the C-MAP strategy based on consensus signatures. With our approach, we found connections between drugs based on transcriptional responses and paved the way for systematic design and analyses of drug synergies. Citation Format: Marc Hafner, Mario Niepel, Qiaonan Duan, Xiaodong Lu, Aravind Subramanian, Avi Ma'ayan, Peter K. Sorger. Transcriptional landscape of drug response guides the design of specific and potent drug combinations. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR16.

Inhibition of epithelial growth factor receptor can play an important role in reducing cell growth and survival in adrenocortical tumors

Medical treatment of adrenocortical carcinoma (ACC) is still far from optimal, since even molecular targeted therapy failed to demonstrate striking results. Clinical trials enrolling ACC patients with high tissue vascular endothelial growth factor receptor (VEGFR) expression levels showed controversial results after treatment with Sunitinib, possibly due to variability in the expression of drug targets, which include epidermal growth factor receptor (EGFR). To better clarify this issue, we evaluated whether VEGFR may play a crucial role in ACC responsiveness to Sunitinib and whether EGFR may represent an alternative target in ACC medical treatment, by employing two ACC cell lines, the NCI-H295 and SW13 cells lines, and adrenocortical tissues primary cultures. Our data show that VEGF/VEGFR system may not be crucial in modulating ACC proliferation and responsiveness to Sunitinib. In addition, by cell viability, proliferation and caspase activation assays we found that Sunitinib inhibits adrenocortical cell viability acting, at least in part, through EGFR, that, in turn, is crucial for EGF proliferative effect on adrenocortical cells. The latter depends, at least in part, on ERK 1/2 activation. An EGFR selective inhibitor was highly effective in reducing cell viability in an adrenocortical tumor primary culture and in the SW13 cells, which express high EGFR levels. Our results suggest that EGFR inhibitors could represent effective therapeutic tools in ACC patients whose tumors express high EGFR levels, that, in turn, may be considered a predictive factor of response. Accurate molecular tumor profiling is crucial to predict drug efficacy and to tailor ACC patients therapeutic approach.

Abstract B53: Predicting efficacy of chemopreventive agents in animal tumor assays by statistical modeling

Abstract NCI's Division of Cancer Prevention has over 25 years entered some 800 agents into a program designed to test cancer preventive efficacy. Early in the testing pathway are 2 sequential critical steps: (1) in vitro/in vivo morphologic assays and, (2) for agents successful in step 1, cancer-preventive testing (measured in terms of tumor incidence and multiplicity reduction) in animal tumor assays. The ultimate intention is to follow successful testing in animals with entry into clinical trial testing in humans. To facilitate the optimal selection of appropriate morphologic tests for specific disease-site animal tumor models, we undertook the current project. We evaluated the success of our strategy by determining how accurately the earlier stage (morphologic) assays predict efficacy in the later-stage (animal tumor) assays. Focusing on 210 agents that were tested in both morphologic and animal tumor assays, we carried out statistical modeling of how well the 6 most commonly used morphologic assays predicted drug efficacy in animal tumor models. Using multimodel inference, three statistical models were generated to evaluate the ability of these 6 morphologic assays to predict tumor outcomes in 3 different sets of animal tumor assays: (1) all tumor types, (2) breast cancer only, and (3) colon cancer only. Using this statistical modeling approach, each morphologic assay was assigned a value reflecting how strongly it predicted outcomes in each of the 3 different sets of animal tumor assays. The goal is to use predictive models such as these to guide our future decision-making with respect to selection of preventive agents as well as morphologic and animal tumor assays. In this manner, we hope to improve the efficiency of our overall approach to chemoprevention agent development. Citation Format: Barbara K. Dunn, Vernon E. Steele, Richard M. Fagerstrom, Carol F. Topp, David Ransohoff, Leslie G. Ford, Barnett S. Kramer. Predicting efficacy of chemopreventive agents in animal tumor assays by statistical modeling. [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr B53.

Predictive Value Tools as an Aid in Chemopreventive Agent Development.

Over 25 years, the National Cancer Institute's Division of Cancer Prevention has entered some 800 agents into a chemopreventive agent testing program. Two critical steps involve: 1) in vitro/in vivo morphologic assays and 2) animal tumor assays (incidence/multiplicity reduction). We sought to determine how accurately the earlier-stage (morphologic) assays predict efficacy in the later-stage (animal tumor) assays. Focusing on 210 agents tested in both morphologic and animal tumor assays, we carried out statistical modeling of how well the six most commonly used morphologic assays predicted drug efficacy in animal tumor assays. Using multimodel inference, three statistical models were generated to evaluate the ability of these six morphologic assays to predict tumor outcomes in three different sets of animal tumor assays: 1) all tumor types, 2) mammary cancer only, and 3) colon cancer only. Using this statistical modeling approach, each morphologic assay was assigned a value reflecting how strongly it predicted outcomes in each of the three different sets of animal tumor assays. We demonstrated differences in the predictive value of specific morphologic assays for positive animal tumor assay results. Some of the morphologic assays were strongly predictive of meaningful positive efficacy outcomes in animal tumor assays representing specific cancer types, particularly the aberrant crypt focus (ACF) assay for colon cancer. Moreover, less strongly predictive assays can be combined and sequenced, resulting in enhanced composite predictive ability. Predictive models such as these could be used to guide selection of preventive agents as well as morphologic and animal tumor assays, thereby improving the efficiency of our approach to chemopreventive agent development.

Predicting Drug Response in Human Prostate Cancer from Preclinical Analysis of In Vivo Mouse Models.

Although genetically engineered mouse (GEM) models are often used to evaluate cancer therapies, extrapolation of such preclinical data to human cancer can be challenging. Here, we introduce an approach that uses drug perturbation data from GEM models to predict drug efficacy in human cancer. Network-based analysis of expression profiles from invivo treatment of GEM models identified drugs and drug combinations that inhibit the activity of FOXM1 and CENPF, which are master regulators of prostate cancer malignancy. Validation of mouse and human prostate cancer models confirmed the specificity and synergy of a predicted drug combination to abrogate FOXM1/CENPF activity and inhibit tumorigenicity. Network-based analysis of treatment signatures from GEM models identified treatment-responsive genes in human prostate cancer that are potential biomarkers of patient response. More generally, this approach allows systematic identification of drugs that inhibit tumor dependencies, thereby improving the utility of GEM models for prioritizing drugs for clinical evaluation.

A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.

Abstract 1698: Systems pathology for characterization of cancer model systems in a multicenter IMI-PREDECT project

Abstract Despite of our increased understanding of cancer biology, the majority of anti-cancer therapies fail at late-stage clinical trials. Thus, there is an urgent need to develop and validate novel preclinical models that could predict drug efficacy in humans. For this purpose, as a part of IMI-PREDECT public-private research consortium, this study describes methodology and infrastructure for characterization and comparison of preclinical models for drug target validation applying a systems pathology approach. Formalin-fixed paraffin-embedded (FFPE) samples from 1050 different in vitro and in vivo models of breast, prostate and lung cancers, as well as 364 clinical tumors from the same origin, were collected from 26 PREDECT collaborators across the EU. We established standard operating procedures for central processing of FFPE samples, including tissue microarray (TMA) construction and immunohistochemistry (IHC) with 15 different antibodies (CK8, Ki67, p-histone H3, ER, AR, p-AKT, p-ERK, p-p38, γH2AX, cleaved caspase 3, p-MET, HIF1α, p63, vimentin, E-cadherin). We constructed 50 TMA blocks, from which sections were cut and stained as well as digitized using whole slide imaging. Images were hosted on a WebMicroscope digital pathology platform and sample metadata on a PREDECT Metadata database (MBase). We developed image analysis methods for the detection and quantification of IHC biomarkers in the 48,800 stained TMA spots. As a proof-of-concept, we compared MCF-7 on several preclinical platforms including cell cultures, xenografts and xenograft tissue slices. Our results of the integrated biomarker phenotype suggest that of the various MCF-7 in vivo and ex vivo complex cell culture models, the xenograft tissue slice model was the most similar model platform to human clinical samples. In summary, we established a systems pathology approach to analyse and compare novel preclinical cancer models with IHC and digital imaging. The intention is that this large database will be made publicly available on the web as images and summary data that could be broadly useful to the community of cancer researchers and drug developers in comparing cancer model systems. The established infrastructure and workflow integrating molecular and digital pathology in a large-scale consortium setting could be applied to quantitative characterisation of consortium data in collaborations similar to PREDECT. Citation Format: Sami Blom, Yinhai Wang, Tauno Metsalu, Tiina Vesterinen, Teijo Pellinen, Anne Grote, Nina Linder, Jenni Säilä, Katja Välimäki, Ruusu-Maria Kovanen, Outi Monni, Panu Kovanen, Emma Davies, Kristin Stock, Marta Estrada, Georgios Sflomos, Sylvia Grünewald, Catarina Brito, Julia Schüler, Ronald de Hoogt, Cathrin Brisken, Heiko van der Kuip, Wytske van Weerden, Simon Barry, Wolgang Sommergruber, Elizabeth Anderson, Matthias Nees, Juha Klefström, Jaak Vilo, Emmy Verschuren, Ralph Graeser, John Hickman, Johan Lundin, Olli Kallioniemi. Systems pathology for characterization of cancer model systems in a multicenter IMI-PREDECT project. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1698. doi:10.1158/1538-7445.AM2015-1698

Robotic printing and drug testing of 384-well tumor spheroids.

A major impediment to anti-cancer drug development is the lack of a reliable and inexpensive tumor model to test the efficacy of candidate compounds. This need has emerged due to the insufficiency of widely-used monolayer cultures to predict drug efficacy in vivo. Spheroids, 3D compact clusters of cancer cells, mimic important characteristics of tumors and provide a tissue analog for drug testing. Here we present a novel spheroid formation microtechnology that is simple to use and allows high throughput drug screening in 384-microwell plates. This approach is based on a polymeric aqueous two-phase system. The denser aqueous phase is mixed with cancer cells at a desired density. Using a robotic liquid handler, a drop of this cell suspension is dispensed into each well of a 384-microwell plate containing the second, immersion aqueous phase. Cancer cells remain contained in the drop, which rests on the well bottom, and form a spheroid during incubation. The use of liquid handling robotics ensures precise dispensing of a single drop, resulting in a single spheroid per well and homogenously sized spheroids within each plate. We confirmed the consistency of production of spheroids and demonstrated their biological relevance to tumors. A proof of concept study with spheroids of triple negative breast cancer cells treated with a standard chemotherapeutic compound, doxorubicin, showed the potential of this method for drug testing. This spheroid culture microtechnology presents key advantages over existing methods such as the ease of drug and viability reagent addition, ability to analyze spheroids without transferring them to a new plate, and the elimination of the need for specialized plates or devices to form spheroids. Incorporating this technology in anti-cancer drug development pipeline will help examine the efficacy of drug candidates more effectively and expedite discovery of novel drugs.

Quantitative modeling of dose-response and drug combination based on pathway network.

BackgroundQuantitative description of dose–response of a drug for complex systems is essential for treatment of diseases and drug discovery. Given the growth of large-scale biological data obtained by multi-level assays, computational modeling has become an important approach to understand the mechanism of drug action. However, due to complicated interactions between drugs and cellular targets, the prediction of drug efficacy is a challenge, especially for complex systems. And the biological systems can be regarded as networks, where nodes represent molecular entities (DNA, RNA, protein and small compound) and processes, edges represent the relationships between nodes. Thus we combine biological pathway-based network modeling and molecular docking to evaluate drug efficacy.ResultsNetwork efficiency (NE) and network flux (NF) are both global measures of the network connectivity. In this work, we used NE and NF to quantitatively evaluate the inhibitory effects of compounds against the lipopolysaccharide-induced production of prostaglandin E2. The edge values of the pathway network of this biological process were reset according to the Michaelis-Menten equation, which used the binding constant and drug concentration to determine the degree of inhibition of the target protein in the pathway. The combination of NE and NF was adopted to evaluate the inhibitory effects. The dose–response curve was sigmoid and the EC50 values of 5 compounds were in good agreement with experimental results (R2 = 0.93). Moreover, we found that 2 drugs produced maximal synergism when they were combined according to the ratio between each EC50.ConclusionsThis quantitative model has the ability to predict the dose–response relationships of single drug and drug combination in the context of the pathway network of biological process. These findings are valuable for the evaluation of drug efficacy and thus provide an effective approach for pathway network-based drug discovery.

Utilization of Genomic Biomarkers for Post-marketing Safety of Drugs

To prevent adverse drug reactions in the post-marketing phase, therapeutic drug monitoring and various laboratory tests have been used for decades. Recently, data on associations between drug adverse reactions and biomarkers based on "omics" technologies/studies have been increasing. Using genomic biomarkers, patients at high risk for developing side effects can be distinguished before initiating medical treatment, allowing the choice of an appropriate drug/initial dosage regimen. Biomarkers based on proteomics or metabolomics can detect the onset of adverse reactions at an earlier stage than can be accomplished with classical laboratory tests. However, the clinical use of drug safety-related biomarkers is still limited compared with biomarkers that predict drug efficacy of, for example, molecular-targeted drugs. In this symposium, genomic biomarkers associated with the safety of anticancer drugs and idiosyncratic adverse reactions are introduced and compared between Japan and other countries. Prospective studies evaluating the application of screening tests to prevent adverse drug reactions are also shown, and steps necessary to accelerate the use of drug safety-related biomarkers are discussed.

Translating slow-binding inhibition kinetics into cellular and in vivo effects.

Many drug candidates fail in clinical trials owing to a lack of efficacy from limited target engagement or an insufficient therapeutic index. Minimizing off-target effects while retaining the desired pharmacodynamic (PD) response can be achieved by reduced exposure for drugs that display kinetic selectivity in which the drug-target complex has a longer half-life than off-target-drug complexes. However, though slow-binding inhibition kinetics are a key feature of many marketed drugs, prospective tools that integrate drug-target residence time into predictions of drug efficacy are lacking, hindering the integration of drug-target kinetics into the drug discovery cascade. Here we describe a mechanistic PD model that includes drug-target kinetic parameters, including the on- and off-rates for the formation and breakdown of the drug-target complex. We demonstrate the utility of this model by using it to predict dose response curves for inhibitors of the LpxC enzyme from Pseudomonas aeruginosa in an animal model of infection.

Predicting drug sensitivity by 3D cell culture models

Because of recent failures of novel drugs in phase II and III trials innovative in vitro models are needed that may predict drug efficacy in patients. Although, numerous modifications of traditional cell culture and animal models were undertaken significant improvements in cell culture- based drug development have not been achieved. Three-dimensional (3D) cell culture may represents an modern alternative to bridge the gap between 2D cell cultures and animal models.