Innovations in Pharmacokinetics Labs: The Future of Drug Development

One of the primary objectives of drug development is to design and develop safe and effective compounds. A significant responsibility for bringing safe and effective drugs falls on understanding the pharmacokinetic properties of a drug candidate. These properties include the absorption, distribution, metabolism, and excretion profile observed during the drug discovery and development phase.

Other pharmacokinetic parameters such as in vitro permeability mechanism, metabolic stability, half-life, plasma protein binding, and tissue accumulation are other parameters crucial during the early stages of drug development. Besides, bioavailability and bioequivalence studies for generic drug products depend on assessing PK parameters. By definition, bioequivalence refers to the similarity between the bioavailability of two drug products. 

With new technology and screening tools, pharmacokinetic CROs have several promising methods to analyze PK parameters. Hence the current article focuses on innovations in pharmacokinetic labs that may positively impact the future of drug development. 

Innovations in pharmacokinetic labs

Screening and identifying potential lead compounds is a time-intensive and expensive process. Therefore in silico models related to PK properties saves time, money, and resources. Besides, they are beneficial in discarding drug compounds with unsuitable characteristics early during the screening process. 

Today several free online tools are available for assessing PK characteristics. Such availability of reliable means helps avoid the need for constructing and validating local models. Besides, some tools can even predict toxicity that can be combined with individual experimental goals. 

The tool AdmetSAR launched in 2012 had 27 predictive tools, and its recent version released in 2019 has around 47 models to predict PK and toxicity parameters. The models were developed and validated via machine learning techniques such as support vector machines, random forest, and deep learning. Besides, specific endpoints are used to obtain individual models. The models were presented and validated with acceptable statistical matrices such as accuracy, specificity, and sensitivity, and domain applicability was obtained using properties such as molecular weight and number of rings. These assessments generated three possible results, a) inside the domain, b) outside of the domain, and c) an indicator predicting when the compound is around 97% of its training set. Besides, another version called AdmetOpt can optimize drug compounds based on their PK properties. 

Moreover, several similar tools are available, including CypRules, DrugMint, FAFDrugs, MetaPred, MetStabOn, NERDD, and many more, each with unique advantages. However, one must observe all technical aspects, such as confidence levels, and validate these tools beyond their applications. 

The developed pharmacokinetic drug data is ideal when researchers combine experimental models with computational data related to physiological processes. This integration is necessary as physiological processes can help generate data around metabolic pathways, biological targets, and specific conditions that are too complex to be understood using computational data or laboratory processes. Technology has now become an integral component of understanding biological information. Therefore many studies are now employing in silico techniques while focusing on integrated in vitro and in vivo approaches for a better understanding of the pharmacokinetic properties of a product. 

In conclusion

Technological advances in pharmacokinetic labs will eventually help us better predict and determine PK parameters as early as possible to design and conduct subsequent clinical studies and post-marketing analysis. 

Must Read: Pharmacokinetic Studies vs. Pharmacodynamic Studies: A Comparative Analysis

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