The biosimilar pathway in the USA: An analysis of the innovator company and biosimilar company perspectives and beyond

In order to improve access to costly biological treatments, a biosimilar pathway in the United States of America (USA) was enacted under the Biologics Price Competition and Innovation Act (BPCI Act) of 2009. The aim of the present study was to investigate how the health policy, the establishment of the biosimilar pathway, influenced related companies by studying their respective perspectives and strategies revealed in literatures and publicly available resources. Perspectives of companies reveal the points of concern for the biosimilar pathway, such as data requirements, patents, interchangeability, naming, and exclusivity. Innovator companies may utilize expedited programs for serious conditions, enhance patent protection, launch programs for life-cycle extension, and develop biosimilars as well. The biosimilar companies overcoming technical barriers might need to gather convincing evidence to facilitate market penetration as well as to distinguish their products from those of other biosimilar competitors. More challenges are expected for innovator companies if international harmonization takes place, which might be worth further investigation.     

Managing core resources for genomics and proteomics

Recent years have seen an explosive growth in biological data, which is often not published in a conventional sense but rather deposited in a database. This trend and the need for computational analyses of the data make databases essential tools for biological research. Data from a variety of sources, covering a wide range of biological information, are stored in different, often quite specialized, databases. The provision of such databases as useful resources for the scientific community is a demanding task since the data not only have to be stored in a consistent way, but also have to be easily accessible and highly integrated with other databases. Furthermore, it is necessary to provide users with effective tools to search the databases and to analyze the data. At the European Bioinformatics Institute (EBI), we develop and maintain a number of biological databases and provide a variety of bioinformatics tools to facilitate database and similarity searches and data analysis. In this review, we will provide examples of the core resources maintained at the EBI and summarize important issues of database management of such resources.     

The public road to high-quality curated biological pathways

Biological pathways are abstract and functional visual representations of existing biological knowledge. By mapping high-throughput data on these representations, changes and patterns in biological systems on the genetic, metabolic and protein level are instantly assessable. Many public domain repositories exist for storing biological pathways, each applying its own conventions and storage format. A pathway-based content review of these repositories reveals that none of them are comprehensive. To address this issue, we apply a general workflow to create curated biological pathways, in which we combine three content sources: public domain databases, literature and experts. In this workflow all content of a particular biological pathway is manually retrieved from biological pathway databases and literature, after which this content is compared, combined and subsequently curated by experts. From the curated content, new biological pathways can be created for a pathway analysis tool of choice and distributed among its user base. We applied this procedure to construct high-quality curated biological pathways involved in human fatty acid metabolism.     

Estimating toxicity-related biological pathway altering doses for high-throughput chemical risk assessment

We describe a framework for estimating the human dose at which a chemical significantly alters a biological pathway in vivo, making use of in vitro assay data and an in vitro-derived pharmacokinetic model, coupled with estimates of population variability and uncertainty. The quantity we calculate, the biological pathway altering dose (BPAD), is analogous to current risk assessment metrics in that it combines dose-response data with analysis of uncertainty and population variability to arrive at conservative exposure limits. The analogy is closest when perturbation of a pathway is a key event in the mode of action (MOA) leading to a specified adverse outcome. Because BPADs are derived from relatively inexpensive, high-throughput screening (HTS) in vitro data, this approach can be applied to high-throughput risk assessments (HTRA) for thousands of data-poor environmental chemicals. We envisage the first step of HTRA to be an assessment of in vitro concentration-response relationships across biologically important pathways to derive biological pathway altering concentrations (BPAC). Pharmacokinetic (PK) modeling is then used to estimate the in vivo doses required to achieve the BPACs in the blood at steady state. Uncertainty and variability are incorporated in both the BPAC and the PK parameters and then combined to yield a probability distribution for the dose required to perturb the critical pathway. We finally define the BPADL as the lower confidence bound of this pathway-altering dose. This perspective outlines a framework for using HTRA to estimate BPAD values; provides examples of the use of this approach, including a comparison of BPAD values with published dose-response data from in vivo studies; and discusses challenges and alternative formulations.     

Search for bioactive natural products from medicinal plants of Bangladesh

In our continuous search for bioactive natural products from natural resources, we explored medicinal plants of Bangladesh, targeting cancer-related tumor necrosis factor-related apoptosis-inducing ligand-signaling pathway, along with some other biological activities such as prostaglandin inhibitory activity, 1,1-diphenyl-2-picrylhydrazyl free-radical-scavenging activity, and cell growth inhibitory activity. Along with this, we describe a short field study on Sundarbans mangrove forests, Bangladesh, in the review.     

Quinone methide formations in the Cu(2+)-induced oxidation of a diterpenone catechol and concurrent damage on DNA

Terpene quinone methides have been isolated from natural resources and exhibit broad biological activities against bacteria, fungi, and tumor cells through the reactive quinone methide (QM) moiety. The biological potential of the oxidation of terpene QM precursors, however, has not been assessed even though Cu(2+)-induced oxidation of catechol shows detrimental effects on cells. In this study, a diterpenone catechol was investigated as a precursor of terpene QM under aqueous conditions in the presence of Cu2+. Direct QM formation was implied in the Cu(2+)-induced oxidation through the study of thiol addition using HPLC and ESI-MS analysis. In addition, oxidation of the initial QM adduct to a second-QM intermediate was observed. The direct QM oxidation pathway may be unique for diterpenone catechol in the Cu(2+)-induced oxidation and is an addition to the reported isomerization pathway of o-quinones to QMs. The DNA damage by the Cu(2+)-induced oxidation of diterpenone catechol was assessed on a short duplex DNA target. Both direct DNA cleavage and nucleobase oxidation were observed extensively by in situ-generated hydroxyl radicals.     

Novel approaches to develop community-built biological network models for potential drug discovery

Introduction: Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data.

Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described.

Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients’ differences with respect to disease prevention, diagnosis, and therapy outcome.     

Hedgehog-Pat/Smo-Gli信号通路介导肿瘤的侵袭转移研究进展

Hedgehog-Pat/Smo-Gli信号转导通路是一条高度保守的信号转导通路,从果蝇到脊椎动物的胚胎发育、组织分化等特定的过程中,该通路调控着细胞的分化与增殖,发挥着中轴器官的形态发生作用。该通路在肿瘤特别是在上皮类肿瘤发生中同样发挥着重要的作用。近来发现该通路与肿瘤的侵袭转移关系密切。该文就该通路及其在肿瘤侵袭转移中的研究进展作一综述。     

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.     

Isoprenoid precursor biosynthesis offers potential targets for drug discovery against diseases caused by apicomplexan parasites

Two, simple, C5 compounds, dimethylally diphosphate and isopentenyl diphosphate, are the universal precursors of isoprenoids, a large family of natural products involved in numerous important biological processes. Two distinct biosynthetic pathways have evolved to supply these precursors. Humans use the mevalonate route whilst many species of bacteria including important pathogens, plant chloroplasts and apicomplexan parasites exploit the non-mevalonate pathway. The absence from humans, combined with genetic and chemical validation suggests that the non-mevalonate pathway holds the potential to support new drug discovery programmes targeting Gram-negative bacteria and the apicomplexan parasites responsible for causing serious human diseases, and also infections of veterinary importance. The non-mevalonate pathway relies on eight enzyme-catalyzed stages exploiting a range of cofactors and metal ions. A wealth of structural and mechanistic data, mainly derived from studies of bacterial enzymes, now exists for most components of the pathway and these will be described. Particular attention will be paid to how these data inform on the apicomplexan orthologues concentrating on the enzymes from Plasmodium spp. these cause malaria, one the most important parasitic diseases in the world today.     

Systems-ADME/Tox: resources and network approaches

The increasing cost of drug development is partially due to our failure to identify undesirable compounds at an early enough stage of development. The application of higher throughput screening methods have resulted in the generation of very large datasets from cells in vitro or from in vivo experiments following the treatment with drugs or known toxins. In recent years the development of systems biology, databases and pathway software has enabled the analysis of the high-throughput data in the context of the whole cell. One of the latest technology paradigms to be applied alongside the existing in vitro and computational models for absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) involves the integration of complex multidimensional datasets, termed toxicogenomics. The goal is to provide a more complete understanding of the effects a molecule might have on the entire biological system. However, due to the sheer complexity of this data it may be necessary to apply one or more different types of computational approaches that have as yet not been fully utilized in this field. The present review describes the data generated currently and introduces computational approaches as a component of ADME/Tox. These methods include network algorithms and manually curated databases of interactions that have been separately classified under systems biology methods. The integration of these disparate tools will result in systems-ADME/Tox and it is important to understand exactly what data resources and technologies are available and applicable. Examples of networks derived with important drug transporters and drug metabolizing enzymes are provided to demonstrate the network technologies.     

'The 39 steps' in gene expression profiling: critical issues and proposed best practices for microarray experiments

Gene expression microarrays have been used widely to address increasingly complex biological questions and to produce an unprecedented amount of data, but have yet to realize their full potential. The interpretation of microarray data remains a major challenge because of the complexity of the underlying biological networks. To gather meaningful expression data, it is crucial to develop standardized approaches for vigilant study design, controlled annotation of resources, careful quality control of experiments, robust statistics, and data registration and storage. This article reviews the steps needed in the design and execution of valid microarray experiments so that global gene expression data can play a major role in the pursuit of future biological discoveries that will impact drug development.     

Chemical databases for environmental health and clinical research

The increasing number of publicly available biological databases reflects the evolving need for managing and evaluating abundant and complex data in biological, clinical and computational research. Currently there are over 1000 biologically relevant databases in the public domain with varied content and diverse approaches to capturing and presenting data. This review summarizes the comparatively small niche of sophisticated databases and other resources that aim to enhance understanding of chemicals and their biological actions. The databases reviewed include 1 that emphasizes environmental chemicals and 9 that emphasize drugs and small molecules. These databases and their associated resources are incrementally strengthening the expanding field of toxicogenomics-based research by providing centralized sources of manually and computationally curated datasets and highly sophisticated tools for the meta-analysis of continually increasing environmental chemical, drug and small-molecule datasets.     

Comparison of two different stochastic models for extracting protein regulatory pathways using Bayesian networks

Toxicoproteomics integrates traditional toxicology and systems biology and seeks to infer the architecture of biochemical pathways in biological systems that are affected by and respond to chemical and environmental exposures. Different reverse engineering methods for extracting biochemical regulatory networks from data have been proposed and it is important to understand their relative strengths and weaknesses. To shed some light onto this problem, Werhli et al. (2006) cross-compared three widely used methodologies, relevance networks, graphical Gaussian models, and Bayesian networks (BN), on real cytometric and synthetic expression data. This study continues with the evaluation and compares the learning performances of two different stochastic models (BGe and BDe) for BN. Cytometric protein expression data from the RAF-signaling pathway were used for the cross-method comparison. Understanding this pathway is an important task, as it is known that RAF is a critical signaling protein whose deregulation leads to carcinogenesis. When the more flexible BDe model is employed, a data discretization, which usually incurs an inevitable information loss, is needed. However, the results of the study reveal that the BDe model is preferable to the BGe model when a sufficiently large number of observations from the pathway are available.     

Resources and tools for investigating biomolecular networks in mammals

Molecular databases serve as primary information resources for the analysis of biological networks providing an essential and invaluable treasure for information exploration. Tools for projecting experimental data sets onto known functional information are a major need to support the analysis of samples produced in clinical research. A new concept is the notation of functional modules, i.e. the characterisation of sets of proteins that perform a defined biological function in cooperation. The determination and analysis of functional modules overcome the limitations of the analysis of individual genes and their properties. Although functional modules are not suitable to fully capture systems properties, they have the potential to unify the information generated by different types of experiments. We describe advances related to the problem of integrating heterogeneous data sets into functional modules for mouse and/or human cellular networks based on publicly available data resources, including advances in the design of ontologies for functional classification, problems of automatic protein functional annotation and integration of microarray data.     

Integrative identification of core genetic regulatory modules via a structural model-based clustering method

Regulatory modules play fundamental roles in processing and dispatching signals in cell life cycle. Although current clustering methods may reduce data complexity to lower dimension, they tend to neglect biological meanings within high-throughput data. We propose a module-detection algorithm through defining network activity measures and associating them through a weighted clustering approach. We verify our method on diverse models and it provides a unique perspective for analysing model dynamics and expression data, especially with consideration of inherent biological meanings. As it can detect core regulatory modules effectively, it facilitates pathway/network modelling in systems biology.     

Genomic approaches in dissecting complex biological pathways

Advances in genomic research have provided many types of large-scale data that contain rich information on various biological pathways. Intensive efforts have been made to qualitatively or quantitatively model biological pathways using these genomic data. Some general network properties, such as the scale-free property and network motifs, have been discussed and various network models have been applied to reconstruct pathways. However, there is a lack of systematic integration of prior knowledge and different genomic data in these analyses. In this review, we discuss pathway reconstruction under the consideration of the complexity embedded in the biological system, and the global and local properties of biological pathways. We review major methodologies, including clustering methods, scale-free networks models, Bayesian networks models, Boolean networks models, systems of differential equations, and data integration methods. We focus on the difficulty of each methodology in modeling biological pathways, and emphasize that different models capture different aspects of biological pathways or genomic data. The 'noisy' large-scale genomic data require the mathematical models and computational methods to be both robust and identifiable. In addition, we believe that ideal models should have the capability of incorporating various data types and these models need to be assessed through rigorous comparisons with empirical data.