What is Pharmacodynamics?

In drug discovery trials, the potential for a molecule to do harm to an organism must be known accurately. Understanding how a substance under investigation behaves once in the body informs the design of clinical trials and provides invaluable data for the release of new drugs onto the market. One field of study which can help to inform drug discovery and design is pharmacodynamics.

But what is pharmacodynamics? This article will provide a brief overview of the subject and how it is used in drug discovery preclinical trials. A brief discussion on current research into increasing the scalability of PK/PD (pharmacokinetic/pharmacodynamic) models, which are commonly used in preclinical studies, will also be presented.

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Pharmacodynamics – an overview

Pharmacodynamics (PD and pharmacokinetics (PK) constitute the two main branches of pharmacology. Pharmacodynamics studies the biological and physiological effects of drugs on an organism, whereas pharmacokinetics studies how the organism affects the drug. Pharmacodynamics and pharmacokinetics are often combined in PK/PD models. These models are used extensively in preclinical trials.

Both pharmacodynamics and pharmacokinetics together influence factors such as dosing, drug benefits, and adverse effects. Pharmacodynamics places particular emphasis on dose-response relationships, which are the relationships between the concentration of a drug and its effect, whether negative or positive, upon the organism.

Negative/undesirable effects include the increased probability of cell mutation (otherwise known as carcinogenic activity), induced physiological damage, abnormal chronic conditions, adverse reproductive effects, and lethality. Therefore, knowledge of a drug’s behavior is absolutely vital in drug development studies.

Multicellular pharmacodynamics

Recently, pharmacodynamic concepts have been expanded to include multicellular pharmacodynamics (MCPD). MCPD concepts help researchers to understand the dynamic and static relationships between drugs and multicellular four-dimensional organization in organisms. In this way, a drug’s action upon a minimal multicellular system can be studied both in vivo and in silico.

Networked multicellular pharmacodynamics extends the MCPD concept to include accurate modeling of regulatory genomic networks in combination with signal transduction pathways. With these concepts, the complex interacting components within a cell and how drugs affect them can be studied more effectively.

Why is applying pharmacodynamic models in preclinical trials important?

Adverse effects a drug can have upon an organism can be numerous, as illustrated above. Volunteers for drug trials and researchers need to be assured that the drug under development will not cause unknown side-effects and is safe for use. Therefore, it is of the utmost importance that the action of a substance upon the body is elucidated accurately in preclinical studies.

Preclinical trials need to follow certain regulatory procedures before a drug can move onto human test subjects. Most trials include two animal models, one rodent and one non-rodent. Pharmacodynamic and pharmacokinetic models of the drug’s action provide specific parameters which are then used to inform initial doses in the associated clinical trial.

Scaling pharmacodynamics from preclinical animal studies to humans

One issue with current pharmacodynamic modeling is the scalability of data from preclinical animal studies to human studies and how to accurately predict the behavior of drugs in the human body.

Integrating information obtained from in vitro bioassays and preclinical pharmacological studies in animals helps scientists anticipate any clinical and adverse effects of a drug. PK/PD models have several important drug and system-specific factors, including the time-course and intensity of a drug’s pharmacological effects.

Evolutions in bioanalysis, -omics, and computer software have driven improvements in pharmacodynamics. Comprehensive assessments of the molecular to whole-body pharmacodynamics of diverse drugs have become more possible over the past 50 years. Contemporary PK/PD models continue to evolve.

Due to this evolution and the fact that models seek to emulate systems-level properties, there are several opportunities to scale up pharmacodynamic data. Further refinement of translational PK/PD modeling should provide increased efficiency in critical drug discovery and development steps.

Recent studies have explored the capability of pharmacodynamics to increase scalability. A review by a team led by Donald E Mager highlighted fundamental principles in pharmacodynamics and basic expectations in PK/PD modeling.

A case study on PK/PD modeling efforts for recombinant human erythropoietin was included in the review also. This case study was used to demonstrate pharmacodynamics’ potential for improving understanding of inter-species differences in drug responses.

The extrapolation of in vitro, in silico, and preclinical animal studies to predict the pharmacodynamic properties of drugs in humans is now within reach thanks to studies like this.

In conclusion

Pharmacodynamics, along with pharmacokinetics, is used in preclinical animal studies to predict the behavior of drugs within the human body. The use of PK/PD modeling informs parameters such as dosing and potential side-effects of the drug administered.

With recent advances in pharmacodynamic modeling including increased scalability of data gathered, scientists hope to better understand how drugs behave once in the human body. This will help future researchers develop safer drugs for release onto the market.

Further Reading

• All Preclinical Testing Content
• In Vitro vs In Vivo Preclinical Studies
• What is Toxicity?

Written by

Reginald Davey

Reg Davey is a freelance copywriter and editor based in Nottingham in the United Kingdom. Writing for News Medical represents the coming together of various interests and fields he has been interested and involved in over the years, including Microbiology, Biomedical Sciences, and Environmental Science.