Drug development is a complex and expensive process that involves multiple stages, including drug discovery, development, preclinical testing, and clinical trials, with applicable regulatory approvals at each stage. The average cost of bringing a new drug to market by making them pass through all stages of development is now over $2 billion, and about 90% of drugs fail to get approval from the FDA. Unfortunately, despite significant investments in these stages, many drug candidates fail to make it to the market due to a phenomenon known as drug attrition.
What is drug attrition?
Drug attrition refers to the high rate of failure that drug candidates experience during clinical development, often due to safety concerns, lack of efficacy, or unacceptable toxicity. About half of all drugs that enter clinical trials are discontinued due to safety issues. Drug attrition not only results in significant financial losses for drug developers but also delays the availability of much-needed therapies for patients. To address this challenge, the field of clinical pharmacology has developed several quantitative tools and methodologies that can help reduce drug attrition and improve the success rate of clinical trials.
Clinical pharmacology is a field of study that uses quantitative tools and techniques to understand how drugs interact with the body and how the body processes the drug and to optimize dosing and safety in clinical trials.
Quantitative tools to reduce drug attrition
Quantitative tools of clinical pharmacology can be used to reduce drug attrition by identifying and mitigating safety risks earlier in the development process. These tools use mathematical models to predict the safety and efficacy of drugs in humans. They can be used to identify potential safety concerns based on preclinical data and to design clinical trials that are more likely to identify and assess these risks.
One such tool is pharmacokinetic (PK) modeling, which involves the mathematical analysis of drug exposure in the body over time. PK aka ADME is the study of how drugs are absorbed, distributed, metabolized, and excreted by the body. PK models can be used to predict how a drug will be distributed throughout the body, and how long it will remain in the body or rate of elimination. PK modeling can also be used to predict how a drug will behave in different populations, such as patients with different ages, genders, or disease states. This can help drug developers optimize the dosing regimen, select the most appropriate patient population, and identify potential safety concerns. This information can also be used to design clinical trials that are more likely to identify and assess safety concerns. For example, PK models can be used to predict how a drug will interact with other drugs and to identify potential drug interactions.
Another quantitative tool is pharmacodynamic (PD) modeling, which examines the relationship between drug concentration (exposure) and its effects (response) on the body. PD modeling can help predict the dose-response relationship for a drug and identify the optimal dose for a given patient population. This can help reduce the risk of under- or over-dosing, which can lead to sub-therapeutic dosing or toxicity. PD modeling can also be used to identify the biomarkers that can be used to monitor its efficacy. For example, PD models can be used to predict how a drug will affect the heart, liver, and kidneys. This information can be used to design clinical trials that are more likely to identify and assess these risks.
By integrating PK and PD data, researchers can optimize dosing regimens, identify potential safety concerns, and improve the likelihood of success in clinical trials. In addition to PK and PD modeling, clinical pharmacology also utilizes several other quantitative tools, such as population pharmacokinetics (PopPk), exposure-response (ER) modeling, physiologically based pharmacokinetic (PBPK), and quantitative systems pharmacology (QSP). These tools can help researchers to better understand drug metabolism and distribution, predict drug-drug interactions, and optimize the clinical trial design.
Here are some ways in which these tools can be applied to reduce drug attrition:
- Identify potential safety concerns: Researchers can identify potential safety concerns related to drug interactions, making it easier to make a go-no-go decision.
- Optimize dosing regimens: By understanding the pharmacokinetics and pharmacodynamics of a drug candidate, researchers can design dosing regimens that are more effective and less likely to cause adverse events.
- Conduct population pharmacokinetic studies: These studies can help in understanding the effect of the drug on different patient populations and understand how factors like age, and gender can affect the drug response.
- Simulate clinical trials: Clinical trial simulations can help understand the potential success of it and conduct the trial by modifying the study design accordingly. It can help reduce the number of patients required in each trial or even reduce the number of trials.
- Evaluate drug-drug interactions: Most importantly, the quantitative tools can help understand how drugs can interact early in the drug development process to avoid future delays.
In conclusion, drug attrition is a major challenge for the pharmaceutical industry, but quantitative tools can help to reduce the risk of failure during drug development. PBPK and PD models, as well as other quantitative tools of clinical pharmacology, can help pharmaceutical companies to identify potential safety concerns and improve the efficiency of clinical trials. Standard and quality data is critical to the success of these quantitative methods. Excelra’s clinical data solutions are focused on extracting and curating clinical trial outcome data, biology and pharmacology data, and pharmacokinetic data from various clinical literature. Pharmaceutical companies can improve the success rate of their drug development programs and bring new, safe, and effective drugs to market by leveraging different quantitative tools and Excelra’s clinical data expertise.