Drug Discovery Process Of The Bio Pharmaceutical Industry Biology Essay
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The development of new pharmaceutical products is a long, expensive and uncertain process. It takes an average of 10 to 15 years for a new drug to move from the discovery phase into the marketplace, and the average cost for the development of a new drug is approximately $800 million. Out of 6000 compounds initially screened, only 6 are selected to move onto clinical trials, and out of those 6, only 1 compound is approved by the Food and Drug Administration (FDA) and the product is introduced into the market. The low success rates associated with new drug development is what makes the demand for resources at different stages of the development pipeline highly variable and very difficult to predict. Even after a drug is launched into the market, success is not guaranteed as toxic side effects may erupt when the drug is introduced to a larger sample of diverse population. This unpredictable process together with the company's annual fixed cost of Research and Development creates a major financial risk. Nevertheless, pharmaceutical firms decide to undertake this risky process because of the opportunity to develop a "blockbuster" drug, a drug that generates $1 billion or more each year in sales revenue for the company, and has the capacity to meet unmet medical needs of the population.
To Understand the Disease
Before any potential new medicine can be discovered, scientists work to understand the causes as well as other underlying factors in relation to the disease to be treated as well as possible.
Target Identification (Choose a molecule to target with a drug)
Once they have enough understanding of the underlying cause of a disease, pharmaceutical researchers select a "target" for a potential new medicine. A target is generally a single molecule, such as a gene or protein, which is involved in a particular disease. Even at this early stage in drug discovery it is critical that researchers pick a target that is "drugable," i.e., one that can potentially interact with and be affected by a drug molecule.
Target Validation (Test the target and confirm its role in the disease)
After choosing a potential target, scientists must show that it actually is involved in the disease and can be acted upon by a drug. Target validation is crucial to help scientists avoid research paths that look promising, but ultimately lead to dead ends. Researchers demonstrate that a particular target is relevant to the disease being studied through complicated experiments in both living cells and in animal models of disease.
Various stages in Drug Development Process
The drug development process is highly regulated and follows a number of well-defined steps and milestones.
Discovery and screening stage
Emerging tools in molecular biology, cell biology and combinatorial chemistry help researchers understand diseases and identify specific targets for new drugs. Once a specific target is identified, drug development starts with the screening of a large number of compounds to find the non-toxic compounds with the desired biological effects. Typically, thousands of chemical compounds are tested in test tubes or individual cells (tissue cultures). Drug companies maintain large libraries of newly synthesized or isolated compounds. Compound from these libraries are tested for biological activity.
Preclinical testing involves a series of short term and long term animal and laboratory tests to generate data on if a compound is safe and worthwhile to test on people. The aim of preclinical testing is to understand what happens when the drug is metabolized, as well as to generate information about the optimal dose for the clinical trials. Animal studies provide data on the absorption, distribution and excretion of the compound. The chemical properties of the discovered compounds are studied in significant detail at this step. Steps for synthesis and purification are developed at this time. These help identify any acute toxicity issues that may arise. It usually takes 3-4 years to gather data in support of Investigational New Drug Application (IND). This application notifies the Food and Drug Authority (FDA) of the drug sponsor's intent to conduct clinical research on human. In parallel with the animal studies, the company has to conduct studies to determine how to manufacture reproducible batches over time.
The ultimate goal of clinical trials is to determine whether the drug works well enough in patients. The trials should address: whether the risk of toxic side effects outweighs the therapeutic benefit; which dose regimen provides the best response and the least number of side effects; if the drug is better than existing treatments or not. Clinical trials are divided in three phases
Phase I (PI): In Phase I trials, the candidate drug is tested in people for the first time. These studies are usually conducted with about 20 to 100 healthy volunteers. The main goal of a Phase I trial is to discover if the drug is safe for humans. Researchers look at the pharmacokinetics of a drug: How is it absorbed? How is it metabolized and excreted from the body? They also study the pharmacologics of a drug: effects of the drug on the functioning of the human body. These closely monitored trials are designed to help researchers determine what the safe dosage range is and if it should move on to further development.
Phase II (PII): The goal of this phase is to evaluate the effectiveness of the drug for a particular indication and how the drug behaves in people. These studies typically include 100-500 patients with a target disease or indication, divided into several subgroups. The subgroups are administered the drug in different dosages, by different routes, and on different schedules. Efforts are made to determine the common short term side effects and other risks associated with the drug when used on human beings.
Phase III (PIII): The studies in this phase are conducted over a long term and on a large sample of 1000-1500 patient volunteers. The basic aim of this phase is to generate statistically significant data, about to evaluate the risks and benefits associated with the drug. The effectiveness and safety of the drug is carefully examined and dosing regiments duly noted which will lead to the FDA and the international regulatory agencies to approve the new drug. The results from these studies are used to develop the DRUG LABEL.
The Drug Discovery process has many issues like Long Lead Times and Uncertainty that are plaguing the whole process and causing disturbances/tremors along the lines of New Product Development.
A stochastic programming approach for clinical trial planning in new drug development
Matthew Colvin, Christos T. Maravelias
Due to changing circumstances in the managed-health-care environment, the profit margins of pharmaceutical companies and the productivity of their Research and Development (R&D) pipelines have started to decline; effective patent lives have been shortened, and patents provide lower barriers to entry even while active. Therefore, it is imperative for pharmaceutical companies to manage their R&D pipelines more effectively to reduce the cost of developing new drugs. This is a challenging task due to the highly stochastic nature of the R&D process: if a drug fails a clinical trial, its development stops and all prior investment is lost; if it passes all trials, it enters the marketplace and profits are typically significantly larger than development costs. To effectively plan the clinical trials in the pharmaceutical R&D pipeline, therefore, new systematic stochastic optimization methods are necessary.The paper presents a multi-stage stochastic programming formulation for the scheduling of clinical trials in the pharmaceutical research and development (R&D) pipeline. The stages correspond to time periods, i.e. the planning horizon is divided into multiple periods.
Scenarios are used to account for the endogenous uncertainty (a drug either passes or fails a clinical trial) in clinical trial outcomes. Given a portfolio of potential drugs and limited resources, the model determines which clinical trials (PI, PII, PIII) to be performed in each planning period and scenario in order to maximize the expected net present value of the R&D pipeline. The proposed formulation can be used to address problems of medium size and serves as a basis for the development of advanced models for the management of the pharmaceutical R&D pipeline.
Knowledge networking to support medical new product development
Kannan Mohan, Radhika Jain, Balasubramaniam Ramesh
Pharmaceutical firms depend heavily upon their ability to rapidly develop and introduce new products into the market. Product development speed directly impacts their financial bottom-line as well as their ability to satisfy unmet medical needs of patients. However, development of new medical products is complex and time-consuming. It takes anywhere between 7 and 17 years and several millions to billions of dollars to launch new medical products. Some of the factors contributing to the length, cost, and uncertainty of this process are the stringent regulatory requirements of governmental entities like the FDA requiring the maintenance of design history for every medical product to show that the products were developed as per the approved plan and with extensive clinical trials, medical products are used to treat human beings whose well-being and safety are of utmost importance. Thus, failure of the product can have serious consequences, increasing possibilities for therapeutic intervention brought about by newer technologies and enormous investments required in research and development, and testing.
This paper addresses the issue of developing an approach to seamlessly integrate fragmented knowledge using knowledge networks. Semantic knowledge networks provide the ability to describe and follow the life of a physical or conceptual artefact. These have been used as effective solutions to support knowledge integration in knowledge intensive processes in multiple domains. Motivated by their effectiveness in supporting knowledge intensive processes, the paper proposes the creation and use of knowledge networks to facilitate integration of knowledge fragments that are generated and used in medical NPD. The development of a knowledge network should be guided by the unique
characteristics of the medical NPD domain. The paper also provides the background on the process of medical NPD, along with unique issues in this area.
New product development process and time-to-market in the generic pharmaceutical industry
Janez Prasnikar, Tina Skerlj
This article presents some important factors impacting on the lead-time of new products. In particular, we find a negative relationship between the incorporation of organizational tools and techniques, such as concurrent activity management and time-to-market. Further, there is an appropriate negative relationship between the integration of new product development departments in particular phases of the new product development process and the cycle-time of those phases. Appropriate capacity management and project management also contribute to a shorter lead-time of a new product. However, there are also some particularities of generic pharmaceutical companies. The retargeted products (where an existing product is launched in a new market) have longer time-to-market than completely new products. The generic pharmaceutical industry depends very much on local market conditions and it is often easier to launch new products in already existing markets than to launch existing products in new markets. Further, if the active pharmaceutical ingredient is sourced externally the time-to-market is shorter. The same is true of the external sourcing of the pharmaceutical formulation. Since generic companies often build their competencies in the market rather than on the technology used, strategic alliances and early supplier involvement in the new product development are important factors of their market success.
The Biopharmaceutical industry has many a process to be deeply understood and uniquely mapped, however, I would be looking at the following for the purpose of my project
To understand the Drug Discovery Process and map it with New Product Development - Understand how a pharmaceutical product is produced; identify all the stages from the Pre discovery phase to the Discovery phase and from the Pre clinical phase to the Clinical Phase and map it with New Product Development
Identify the sources for reducing Uncertainty in the Drug Discovery Process - One of the features that restrict the smooth functioning of the Drug Discovery process is 'uncertainty' about the drug in trail. If the drug that is being tested fails the clinical trials phase, all the investment and effort towards drug development is lost, but if it passes all the trials, it enters the marketplace and benefits the company by providing profits that are typically significantly larger than the development costs.
Identify the sources for reducing Lead Time in the Drug Discovery Process - Drug development in the pharmaceutical sector is a lengthy process ranging anywhere from 7 to 17 years and costs the companies billions of dollars. Thus identification of sources for reduction in lead time and appropriate application of those steps would directly influence the costs and help in launching the product quicker than usual into the market.
Interaction with Biopharmaceutical Teams working on the Drug Discovery Process at the Biological Sciences and Biological Engineers (B.S.B.E) department at Indian Institute of Technology, Kanpur
Secondary Research from Scientific Journals
Case study approach in Business Press and Scientific Journals
Introduction to the Drug Discovery Process
Detailed explanation of the Drug Discovery Process
Convergence with the New Product Development
Analysis of the related topic as described in Business Press and other Scientific journals.
An outline of the different approaches available for research
Explanation of the different approaches and their outcomes with respect to the project
Results and Discussions of the case
Relation of the Drug Discovery Process with Management concepts
Conclusions and Future Research
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