Risk In Drug Development Research Articles

A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning.

BackgroundDrug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs. It is challenging to reposition drugs as pharmacological data is large and complex. Subnetwork identification has already been used to simplify the visualization and interpretation of biological data, but it has not been applied to drug repositioning so far. In this paper, we fill this gap by proposing a new Physarum-inspired Prize-Collecting Steiner Tree algorithm to identify subnetworks for drug repositioning.ResultsDrug Similarity Networks (DSN) are generated using the chemical, therapeutic, protein, and phenotype features of drugs. In DSNs, vertex prizes and edge costs represent the similarities and dissimilarities between drugs respectively, and terminals represent drugs in the cardiovascular class, as defined in the Anatomical Therapeutic Chemical classification system. A new Physarum-inspired Prize-Collecting Steiner Tree algorithm is proposed in this paper to identify subnetworks. We apply both the proposed algorithm and the widely-used GW algorithm to identify subnetworks in our 18 generated DSNs. In these DSNs, our proposed algorithm identifies subnetworks with an average Rand Index of 81.1%, while the GW algorithm can only identify subnetworks with an average Rand Index of 64.1%. We select 9 subnetworks with high Rand Index to find drug repositioning opportunities. 10 frequently occurring drugs in these subnetworks are identified as candidates to be repositioned for cardiovascular diseases.ConclusionsWe find evidence to support previous discoveries that nitroglycerin, theophylline and acarbose may be able to be repositioned for cardiovascular diseases. Moreover, we identify seven previously unknown drug candidates that also may interact with the biological cardiovascular system. These discoveries show our proposed Prize-Collecting Steiner Tree approach as a promising strategy for drug repositioning.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0371-3) contains supplementary material, which is available to authorized users.

A Synopsis of the "Influence of Epigenetics, Genetics, and Immunology" Session Part A at the 35th Annual Society of Toxicologic Pathology Symposium.

The overarching theme of the 2016 Society of Toxicology Pathology's Annual Symposium was "The Basis and Relevance of Variation in Toxicologic Responses." Session 4 focused on genetic variation as a potential source for variability in toxicologic responses within nonclinical toxicity studies and further explored how knowledge of genetic traits might enable targeted prospective and retrospective studies in drug development and human health risk assessment. In this session, the influence of both genetic sequence variation and epigenetic modifications on toxicologic responses and their implications for understanding risk were explored. In this overview, the presentations in this session will be summarized, with a goal of exploring the ramifications of genetic and epigenetic variability within and across species for toxicity studies and disseminating information regarding novel tools to harness this variability to advance understanding of toxicologic responses across populations.

Drug development in Alzheimer's disease: the path to 2025.

The global impact of Alzheimer’s disease (AD) continues to increase, and focused efforts are needed to address this immense public health challenge. National leaders have set a goal to prevent or effectively treat AD by 2025. In this paper, we discuss the path to 2025, and what is feasible in this time frame given the realities and challenges of AD drug development, with a focus on disease-modifying therapies (DMTs). Under the current conditions, only drugs currently in late Phase 1 or later will have a chance of being approved by 2025. If pipeline attrition rates remain high, only a few compounds at best will meet this time frame. There is an opportunity to reduce the time and risk of AD drug development through an improvement in trial design; better trial infrastructure; disease registries of well-characterized participant cohorts to help with more rapid enrollment of appropriate study populations; validated biomarkers to better detect disease, determine risk and monitor disease progression as well as predict disease response; more sensitive clinical assessment tools; and faster regulatory review. To implement change requires efforts to build awareness, educate and foster engagement; increase funding for both basic and clinical research; reduce fragmented environments and systems; increase learning from successes and failures; promote data standardization and increase wider data sharing; understand AD at the basic biology level; and rapidly translate new knowledge into clinical development. Improved mechanistic understanding of disease onset and progression is central to more efficient AD drug development and will lead to improved therapeutic approaches and targets. The opportunity for more than a few new therapies by 2025 is small. Accelerating research and clinical development efforts and bringing DMTs to market sooner would have a significant impact on the future societal burden of AD. As these steps are put in place and plans come to fruition, e.g., approval of a DMT, it can be predicted that momentum will build, the process will be self-sustaining, and the path to 2025, and beyond, becomes clearer.

Shortening of the electromechanical window in the ketamine/xylazine-anesthetized guinea pig model to assess pro-arrhythmic risk in early drug development

BackgroundA negative electromechanical window (EMw) was recently proposed as a better preclinical tool than QTc interval to predict clinical pro-arrhythmic potential. As such, we utilized the ketamine/xylazine anesthetized guinea pig to characterize the EMw and QTc interval for a diverse set of reference agents with known clinical pro-arrhythmic potential. Then we determined the clinical proarrhythmia predictive capacity of EMw shortening compared to hERG inhibition or QTc interval prolongation alone. MethodsChanges in EMw and QTc interval by 26 reference agents were evaluated in the ketamine/xylazine-anesthetized guinea pig. Confusion matrix analysis using the hERG, QTc and EMw indexes (hERG IC50, QTc EC5 or the EMw EC−10 divided by their respective free therapeutic maximal plasma concentration) at various folds the therapeutic concentrations was conducted to assess the concordance of each index to predict clinical pro-arrhythmic risk. ResultsShortening of the EMw concomitant to an increase in QTc interval was observed in the GP with known pro-arrhythmic drugs. Non-torsadogenic compounds did not cause EMw shortening, although some prolonged the QTc interval. The preclinical:clinical concordance of the EMw index (88%) was similar (p>0.05) to using QTc interval prolongation alone (85%) but significantly greater (p<0.05) than using hERG inhibition alone (69%). In addition, the specificity when using the EMw (87%) was largely greater (p<0.05) than using QTc interval (73%) or hERG inhibition (60%) alone. When the components of the response (duration of left ventricular pressure (LVP) cycle (QLVPend) or QT interval) that caused EMw shortening were considered, the concordance is further improved (>95%). ConclusionEMw shortening improves QTc interval prolongation recording in early drug development and increases the translatability over existing preclinical tools in predicting clinical arrhythmias.

Trajectory analysis of drug-research trends in pancreatic cancer on PubMed and ClinicalTrials.gov

Increasing interest in developing treatments for pancreatic cancer has led to a surge in publications in the field. Analyses of drug-research trends are needed to minimize risk in anti-cancer drug development. Here, we analyzed publications on anti-cancer drugs extracted from PubMed records and ClinicalTrials datasets. We conducted a drug cluster analysis by proposing the entity Dirichlet Multinomial Regression (eDMR) technique and in-depth network analysis of drug cluster and target proteins. The results show two distinct research clusters in both the ClinicalTrials dataset and the PubMed records. Specifically, various targets associated with anti-cancer drugs are investigated in new drug testing while the diverse chemicals are studied together with a standard therapeutic agent in the academic literature. In addition, our study confirms that drug research published in PubMed is preceded by clinical trials. Although we only evaluate drugs for pancreatic cancer in the present study, our method can be applied to drug-research trends of other diseases.

A model qualification method for mechanistic physiological QSP models to support model-informed drug development.

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision-making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug-specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

Benefit, Risk, and Outcomes in Drug Development: A Systematic Review of Sunitinib.

Little is known about the total patient burden associated with clinical development and where burdens fall most heavily during a drug development program. Our goal was to quantify the total patient burden/benefit in developing a new drug. We measured risk using drug-related adverse events that were grade 3 or higher, benefit by objective response rate, and trial outcomes by whether studies met their primary endpoint with acceptable safety. The differences in risk (death rate) and benefit (overall response rate) between industry and nonindustry trials were analyzed with an inverse-variance weighted fixed effects meta-analysis implemented as a weighted regression analysis. All statistical tests were two-sided. We identified 103 primary publications of sunitinib monotherapy, representing 9092 patients and 3991 patient-years of involvement over 10 years and 32 different malignancies. In total, 1052 patients receiving sunitinib monotherapy experienced objective tumor response (15.7% of intent-to-treat population, 95% confidence interval [CI] = 15.3% to 16.0%), 98 died from drug-related toxicities (1.08%, 95% CI = 1.02% to 1.14%), and at least 1245 experienced grade 3-4 drug-related toxicities (13.7%, 95% CI = 13.3% to 14.1%). Risk/benefit worsened as the development program matured, with several instances of replicated negative studies and almost no positive trials after the first responding malignancies were discovered. Even for a successful drug, the risk/benefit balance of trials was similar to phase I cancer trials in general. Sunitinib monotherapy development showed worsening risk/benefit, and the testing of new indications responded slowly to evidence that sunitinib monotherapy would not extend to new malignancies. Research decision-making should draw on evidence from whole research programs rather than a narrow band of studies in the same indication.

Synthesis, characterization and molecular modelling of a novel dipyridamole supramolecule – X-ray structure, quantum mechanics and molecular dynamics study to comprehend the hydrogen bond structure–activity relationship

Hydrochloride salt formation for Active Pharmaceutical Ingredients (APIs) is the primary choice to impart aqueous solubility and to promote dissolution. Dipyridamole (DIP) is a cardiovascular drug which is practically insoluble in water. We discovered a new form of DIP called as dipyridamole hydrochloride trihydrate (DIPHT), which was prepared by an unusual method of reacting the DIP with hydrated hydrochloric acid (HCl) that was liberated in situ by the reaction of ferric chloride with water. The liberated HCl was consumed as reagent in situ by the scavenger (API) and was converted to a hydrochloride trihydrate. The product was characterized by FTIR, mass spectroscopy, PXRD and DSC. Supramolecular structure of this novel DIPHT was revealed by single crystal XRD. A sustained intramolecular hydrogen bond alliance was found in DIP and the DIPHT. Stability of this hydrogen bond was further evaluated by means of molecular modelling studies. We performed electron calculations using quantum mechanics (QM) on both the base and salt structures to compare their geometry and molecular orbital energy levels. Molecular Dynamics (MD) simulations were also conducted in explicit solvent models to provide more insights into the hydrogen bond strength and conformational preferences of the base and salt structure. Together with QM and MD, we were able to explain the influence of hydrogen bonds on proton uptake activity of DIP and stability of DIP and DIPHT. DIPHT which can dissolve faster than DIP in water may enhance the dissolution and bioavailability of the drug. As the current drug development research is shifting to repurpose the existing drugs in order to subside the untoward risks in new drug development, we believe that DIPHT with its intrinsic aqueous solubility could bring more application for DIP and generate interest within the pharmaceutical industry.

Deciphering the Causal Role of sPLA2s and Lp-PLA2 in Coronary Heart Disease.

Over the last 10 to 15 years, animal and human observational studies have identified elevated levels of both proinflammatory secretory phospholipase A2-IIA and lipoprotein-associated phospholipase A2 as potential risk factors for coronary heart disease. However, Mendelian randomization, a genetic tool to test causality of a biomarker, and phase III randomized controlled trials of inhibitors of theses enzymes (varespladib and darapladib) converged to indicate that elevated levels are unlikely to be themselves causal of coronary heart disease and that inhibition had little or no clinical utility. The concordance of findings from Mendelian randomization and clinical trials suggests that for these 2 drugs, and for other novel biomarkers in future, validation of potential therapeutic targets by genetic studies (such as Mendelian randomization) before embarking on costly phase III randomized controlled trials could increase efficiency and offset the high risk of drug development, thereby facilitating discovery of new therapeutics and mitigating against the exuberant costs of drug development.

Historical foundations of hormesis

The present paper provides an historical assessment of the concept of hormesis and its relationship to homeopathy and modern medicine. It is argued that the dose-response concept was profoundly influenced by the conflict between homeopathy and traditional medicine and that decisions on which dose-response model to adopt were not based on "science" but rater on historical antipathies. While the historical dispute between homeopathy and traditional medicine has long since subsided, their impact upon the field has been enduring and generally unappreciated, profoundly adversely affecting current drug development, therapeutic strategies and environmental risk assessment strategies and policies.

Dynamic monitoring risk of anti-hepatoma new drug development

Risk monitoring of new Chinese patent anti-hepatoma drugs is tracking recognized risks and residual risks, identifying emerging risk and ensure the implementation of the plan, estimating the process of reducing effectiveness. The paper is mainly through understanding the status of Chinese patent anti-hepatoma drugs, the content, characteristic and analysis method of dynamic risk monitoring, and then select the risk control indicators, collect risk information. Finally, puts forward the thought of anti-hepatoma drugs listed evaluation in our country, and try to establish the model of dynamic risk management of anti-hepatoma drugs.

Drug repurposing based on drug-drug interaction.

Given the high risk and lengthy procedure of traditional drug development, drug repurposing is gaining more and more attention. Although many types of drug information have been used to repurpose drugs, drug-drug interaction data, which imply possible physiological effects or targets of drugs, remain unexploited. In this work, similarity of drug interaction was employed to infer similarity of the physiological effects or targets for the drugs. We collected 10,835 drug-drug interactions concerning 1074 drugs, and for 700 of them, drug similarity scores based on drug interaction profiles were computed and rendered using a drug association network with 589 nodes (drugs) and 2375 edges (drug similarity scores). The 589 drugs were clustered into 98 groups with Markov Clustering Algorithm, most of which were significantly correlated with certain drug functions. This indicates that the network can be used to infer the physiological effects of drugs. Furthermore, we evaluated the ability of this drug association network to predict drug targets. The results show that the method is effective for 317 of 561 drugs that have known targets. Comparison of this method with the structure-based approach shows that they are complementary. In summary, this study demonstrates the feasibility of drug repurposing based on drug-drug interaction data.

Orally Inhaled Fixed-Dose Combination Products for the Treatment of Asthma and Chronic Obstructive Pulmonary Disease: Not Simple Math

Over the past decade, orally inhaled fixed-dose combination products (FDCs) have emerged as an important therapeutic class for the treatment of asthma and chronic obstructive pulmonary disease. However, the conceptual simplicity of inhaled FDCs belies both the complexity of their development, and the profound advantages they offer patients. The benefits of combining agents are not merely additive, and range from increased compliance via simple convenience to complex receptor-level synergies. Similarly, though, the development challenges often exceed the sum of their parts. FDC formulation and analytical method development is generally more complex than for two monotherapy products. Likewise, FDC clinical programs can easily eclipse those of their monotherapy peers and their inherent complexity is often furthered by the diverse regulatory requirements for worldwide approval. As such, the proposition of developing an orally inhaled FDC for global registration often represents a significant increase in both the potential rewards and assumed risks of drug development.

Model Steatogenic Compounds (Amiodarone, Valproic Acid, and Tetracycline) Alter Lipid Metabolism by Different Mechanisms in Mouse Liver Slices

Although drug induced steatosis represents a mild type of hepatotoxicity it can progress into more severe non-alcoholic steatohepatitis. Current models used for safety assessment in drug development and chemical risk assessment do not accurately predict steatosis in humans. Therefore, new models need to be developed to screen compounds for steatogenic properties. We have studied the usefulness of mouse precision-cut liver slices (PCLS) as an alternative to animal testing to gain more insight into the mechanisms involved in the steatogenesis. To this end, PCLS were incubated 24 h with the model steatogenic compounds: amiodarone (AMI), valproic acid (VA), and tetracycline (TET). Transcriptome analysis using DNA microarrays was used to identify genes and processes affected by these compounds. AMI and VA upregulated lipid metabolism, whereas processes associated with extracellular matrix remodelling and inflammation were downregulated. TET downregulated mitochondrial functions, lipid metabolism, and fibrosis. Furthermore, on the basis of the transcriptomics data it was hypothesized that all three compounds affect peroxisome proliferator activated-receptor (PPAR) signaling. Application of PPAR reporter assays classified AMI and VA as PPARγ and triple PPARα/(β/δ)/γ agonist, respectively, whereas TET had no effect on any of the PPARs. Some of the differentially expressed genes were considered as potential candidate biomarkers to identify PPAR agonists (i.e. AMI and VA) or compounds impairing mitochondrial functions (i.e. TET). Finally, comparison of our findings with publicly available transcriptomics data showed that a number of processes altered in the mouse PCLS was also affected in mouse livers and human primary hepatocytes exposed to known PPAR agonists. Thus mouse PCLS are a valuable model to identify early mechanisms of action of compounds altering lipid metabolism.

Modifying phase I methodology to facilitate enrolment of molecularly selected patients

Over the last decade, the focus of anticancer drug development has shifted from empirical cytotoxic chemotherapy to mechanism-defined molecularly targeted agents, for which appropriate patient selection at the earliest possible point in drug development is rational and critical to success. With the recently legislated “breakthrough product” definition in the U.S., it may be possible to plan a single trial for registration purposes to confirm a major clinical effect observed in phase I. However, most phase I trial designs remain excessively conservative and are driven by criteria developed for cytotoxic agents with the goal of identifying a “maximum tolerable dose” with acceptable risks to patients. This focus on empiric “most dose with acceptable risk” may be misguided for mechanism-targeting new agents, and this could lead to unnecessary delays, increased costs and even higher risk of missing important signals of activity and benefit. There is a compelling need to modify phase I trial designs to facilitate enrichment in molecularly selected patients who are the most likely to harbour disease driven by the targeted pathway and to avoid unjustified exclusions based on obsolete criteria so that the right subset of patients can participate. After discussion of the main inconsistencies of current phase I designs, we propose a new strategy to facilitate the inclusion of molecularly selected patients, in order to accelerate and mitigate risks in drug development as well as to increase the chance of benefit among trial participants.

Hopes for intractable and rare diseases research.

Hopes for intractable and rare diseases research.

RhoB Differentially Controls Akt Function in Tumor Cells and Stromal Endothelial Cells during Breast Tumorigenesis

Tumors are composed of cancer cells but also a larger number of diverse stromal cells in the tumor microenvironment. Stromal cells provide essential supports to tumor pathophysiology but the distinct characteristics of their signaling networks are not usually considered in developing drugs to target tumors. This oversight potentially confounds proof-of-concept studies and increases drug development risks. Here, we show in established murine and human models of breast cancer how differential regulation of Akt by the small GTPase RhoB in cancer cells or stromal endothelial cells determines their dormancy versus outgrowth when angiogenesis becomes critical. In cancer cells in vitro or in vivo, RhoB functions as a tumor suppressor that restricts EGF receptor (EGFR) cell surface occupancy as well as Akt signaling. However, after activation of the angiogenic switch, RhoB functions as a tumor promoter by sustaining endothelial Akt signaling, growth, and survival of stromal endothelial cells that mediate tumor neoangiogenesis. Altogether, the positive impact of RhoB on angiogenesis and progression supercedes its negative impact in cancer cells themselves. Our findings elucidate the dominant positive role of RhoB in cancer. More generally, they illustrate how differential gene function effects on signaling pathways in the tumor stromal component can complicate the challenge of developing therapeutics to target cancer pathophysiology.

A systematic approach to companion diagnostic development: A case study for omacetaxine (OM) for the treatment of chronic myelogenous leukemia (CML).

100 Background: In the age of genomically informed medicine, therapeutic development carries with it the imperative to employ genomics in patient selection. Physicians expect genomics-based methods to identify treatments likely to be effective, and identify anomalies likely to cause adverse response for a given patient. Companion diagnostics should support such rule-in and rule-out decisions. We demonstrate a systematic approach to companion diagnostics that leverages new methods in translational bioinformatics and clinical economics. CML therapies have been at the forefront of genomically informed medicine. Early TKI inhibitors targeting the BCR-ABL fusion protein are highly effective. With time, however, they induce resistance-creating mutations in many patients. Omacetixine, a translation inhibitor, was expected to help this CML patient subpopulation which has few therapeutic alternatives. We use this well-characterized drug and publicly available data to demonstrate a prospective approach to companion diagnostics. Methods: We used translational bioinformatics, incorporating pathway, cell line, and patient data to identify biologically plausible biomarkers from which alternative companion diagnostic paths were constructed. These alternatives were analyzed using a modified version of the MIT Stratified Medicine Model to assess the clinical economics of each path. Results: From a systematic look at the biology of the disease, the unique mechanism of action of OM and the clinical need, we identified 3 alternative companion diagnostics for OM. Economic analyses quantified the trade-offs of targeting different subpopulations for the indication, clarifying the impact of biomarker selection based on clinical need or biology. Other analyses have shown that eNPV can be halved or doubled based on strategy choice. Conclusions: Combining applied translational bioinformatics and stratified medicine economics provides an effective approach to companion diagnostic selection. This approach can reduce drug-development cost and clinical risk while providing physicians with better genomics-based methods for clinical decision-making.

Drug development risk and the cost of capital

This article analyses the relationship between risk and the cost of capital for projects in different stages of drug development and describes a framework that could improve portfolio optimization strategies.

Translatability scoring in drug development: eight case studies

Translational medicine describes the transfer of basic in vitro and in vivo data into human applications. In the light of low rates of market approvals for new medical entities, better strategies to predict the risk of drug development should be used to increase output and reduce costs. Recently, a scoring system to assess the translatability of early drug projects has been proposed. Here eight drugs from different therapeutic areas have been subjected to a retrospective test-run in this system fictively located at the phase II-III transition. The scores gained here underline the importance of biomarker quality which is pivotal to decrease the risk of the project in all cases. This is particularly evident for gefitinib. The EGFR mutation status is a breakthrough biomarker to predict therapeutic success which made this compound clinically acceptable, and this is plausibly reflected by a considerable increase of the translatability score. For psychiatric and Alzheimer's drugs, and for a CETP-inhibitor, the lack of suitable biomarkers and animal models is reflected by a low translatability score, well correlating with the excessive translational risk in these areas. These case studies document the apparent utility of the scoring system, at least under retrospective conditions, as the scores correlate with the outcomes at the level of market approval. Prospective validation is still missing, but these case studies are encouraging.