The path from discovery through development to commercial drug is a long and costly one. Estimates from the big pharmaceutical companies range between $1.2 - 1.8 billion per new prescription drug approval, taking into consideration the many targets that fail somewhere along the lengthy process, although some smaller companies have been successful with much smaller investments in the vicinity of $500 million. Regardless of the overall cost, it is a significant investment, with the average time to launch a successful drug around 12-15 years. Of the thousands of compounds that manage to reach Phase I clinical trials and continue through the development process, barely 10% will ultimately gain approval for use, thus the process is far from efficient, and these figures do not even take into account all the work done in the discovery phase of the process.
There is clear value, both economic and social, in the successful launch of a new therapeutic. Research and development efforts persist across the board to continue to find new therapeutics to support the growing number of cancers, neurobiological diseases, heart disease along with all the other ailments that threaten our health. Bioline have a full range of products to support the drug development and discovery process from molecular screening for disease characterization through to cloning for functionality tests.
The process of early discovery mainly focuses on characterizing and understanding a disease and associated mechanisms; this work often occurs in clinical research or academic settings. Whereas the process of identifying potential drug candidates can take place in the traditional large scale setting of a pharmaceutical laboratory or, as is happening more often, in smaller, independent laboratories who form commercial entities to take advantage of a potentially viable candidate.
Bioline offers a range of products from individual reagents and targeted kits, right through to large scale assay panels and fully validated assay design services, designed to facilitate a better understanding of specific disease pathways and help to characterize model organisms or interrogate tissue samples. High throughput sequencing products aid in screening and canvassing whole genomes or disease states, while cloning reagents assist with functional investigations. ISO 13485 manufacturing supports transition from pre-clinical to clinical trial phases, offering the standard and quality required for strict regulation purposes.
The initial research phase of drug discovery involves understanding an essential mechanism of the disease in question and forming a hypothesis that inhibition or activation of an element within that mechanism will result in a therapeutic outcome in disease state. From here, targets are identified that are both measurable and accessible to a potential drug molecule. Targets can be in the form of proteins, genes or RNA, and large scale screening and data mining is often employed to assist in this phase of the process. Once a suitable target is identified it undergoes a thorough validation process. Techniques such as gene knockouts and the use of transgenic animal models, allow researchers to study the role of the investigated target within the disease pathway in an attempt to fully understand the effects of manipulation of the target on downstream events. This is important not only in terms of eventual efficacy of the future drug, but also in terms of safety, both key factors that directly impact the likelihood that the drug development process will be successful.
The next phase of discovery focuses on identifying compounds that will interact with the target molecule. Known as the 'hit identification and lead discovery phase' this usually involves high throughput screening of millions of bioactive compounds, using as much automation as possible to streamline the testing process, as well as virtual screens or compound modelling to help narrow down the candidates. The hit identification and lead generation process is a complex phase and requires the workup of specific functionality assays that will allow for accurate measurement of molecule interaction. Compound screens identify potential hits and chemistry programs aim to tweak the interaction to improve specificity, potency or other physiochemical properties of the hit molecules. Once the field of potential hits has narrowed, more specific tissue-based screens can look at in vivo effects of target interaction and ensure that candidates can operate within an intact system. The lead generation work is designed to refine the hit series and through investigating compound structure and activity, maximize the potency and specificity of successful candidates. Candidates are then run through pharmacokinetic/pharmacodynamics studies, usually in animal models, in order to understand dose linearity and metabolic profiling. Successful lead compounds are then transferred to the drug development phase.
Many investigators have identified the need to better understand disease mechanisms before effective therapeutics can be developed, and those within the academic and clinical research field are often best placed to provide this important data. Moreover, identifying and understanding the phenotypic heterogeneity that exists within a patient population can often be key to the success or failure of a drug candidate. Examples exist where approval of drug usage is dependent on upfront genetic screening in order to stratify patient populations.
Another challenge surrounds the use of biomarkers – surrogate or indirect indicators of a patient status or disease state that are often used to measure the effectiveness of a drug or treatment regime. Extensive and costly clinical trials are required to ensure a chosen biomarker can reliably predict clinical efficacy across a patient population and that the measured outcomes are meaningful and reproducible. Reproducibility is in fact a major concern throughout the drug development process, calling for the use of effective and representative modelling systems and activity assays. Although animal models are often employed, results from these systems do not always easily translate to human biology thus, stem cell-based models that more accurately replicate the disease microenvironment are gaining favor in this field.
There are a number of different approaches to drug discovery aimed at maximizing the overall efficiency of the process. The traditional mainstay of drug discovery was housed in the phenotypic approach – where the effects of test compounds on tissues, cells or whole organisms was monitored to see how that molecule modified the disease phenotype. The issue with this approach was that often the mechanism of interaction was not fully understood and there was potential for multiple targets of the test compound, which often led to side-effects in clinical trials downstream. The advent of deep sequencing and the genomics era has been hailed with great expectations to better highlight targets and pathways amenable to drug discovery, as well as improve patient stratification and enhance clinical development programs. Now, as the cost of sequencing continues to drop, this technology not only becomes more accessible for use during discovery, it is also being identified for use as a companion diagnostic to target patients that will more likely benefit from treatment. Bioline offers a range of sequence preparation and support products for both Sanger and next generation sequencing projects to assist both the drug discovery process and development of potential diagnostics.
The systems biology approach to drug discovery and development capitalizes on bioinformatics developments and improvements in computing power, incorporating information form genomic sequencing, biological function databases, microarray data and gene expression databases, and complex calculations of kinetics and molecular interactions, to create computational models within which to test a vast array of targets and candidate molecules. This approach takes advantage of the vast amount of research already done and attempts to make sense of the mountains of data – identifying enriched gene sets or pathway maps, classifying diseases or stratifying patients based on gene expression profiles, or attempting to understand the observed changes in gene signatures of disease states through a range of interrogation or reverse causal reasoning hypotheses. Once candidates are identified in this virtual model, they can be taken through the various practical stages for testing.
Meiotic inheritance is even more controversial; during mammalian sexual reproduction, the epigenome must be reset back to a totipotent state in preparation for the development of the next generation. Given this erasure of somatic epigenetic signatures, it is difficult to understand how epigenetic changes could be transgenerationally or meiotically inheritable. Intriguing multi-generational studies have reportedly identified specific epigenetic markers and associated phenotypic characteristics in offspring linked to environmental exposures such as diet restriction or toxin exposure in grand-parents, suggesting transgenerational epigenetic effects may indeed exist. However many studies in this area have been contested and a mechanism for such inheritance has not yet been defined. There is certainly a wide gap in our knowledge and research efforts persist with mixture of excitement and caution.