Drug safety, effectiveness, and efficacy are studied in humans during clinical trials, and many drugs fall short of Food and Drug Administration (FDA) approval at this stage owing to failures in one or more of these caveats.
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A rigorous understanding of pharmacokinetics, the interactions and transformations of the drug molecule, and pharmacodynamics, the influence of the drug on physiological systems, activity of drugs prior to reaching the stage of clinical trials would allow researchers to narrow down further and optimize drug formulations, improving success rate and lessening the occurrence of negative side effects.
This article will further explore how a better understanding of drug interaction and the mechanism of disease can improve drug development.
How can Drug Activity be Assessed?
One of the most useful metrics in assessing overall drug effectiveness, particularly in cases such as cancer, where drug failure frequently results in death, is overall survival, i.e., the time from drug administration until death from any cause.
However, overall survival data can take several years to mature and become statistically reliable, and thus other methods of assessing the overall effectiveness of chemotherapeutics have been developed. Namely, progression-free survival, the time from administration to when tumor regrowth is observed.
However, this method of drug assessment may only have prognostic value in a relatively narrow range of cancers and is not useful when comparing drug effectiveness in other diseases.
Population pharmacokinetic–pharmacodynamic (PK-PD) modeling is a method of assessing various responses to treatment in an objective way, which can be used to describe disease progression in a prognostic time-to-event manner.
For example, the influence of treating a solid tumor may be assessed by a combination of measurement of tumor volume, circulating biomarker concentration, and observation of negative effects such as myelosuppression.
Population PK models typically describe the concentration of drugs and their metabolic products within ‘compartments’ of the body, i.e., in circulation, the liver, the tumor.
Drug clearance rates for individual patients from each compartment can be examined and compared to that of molecules innately undergoing metabolism within the body, such as creatinine, in order to establish a comparative rate.
The PD response to the concentration of drug in each compartment (PK) can then be established, linking PK drivers with the physiological response, such as the aforementioned tumor shrinkage or change in circulating biomarkers.
Challenges of Pharmacokinetic–Pharmacodynamic (PK-PD) Modeling
A large component of drug development failures can be rooted in the lack of comprehension of the pathophysiology of many diseases and disorders, such as nervous system disorders, resulting in ineffective target identification.
Identifying a relevant target, such as a protein, DNA, or RNA, that contributes to disease is frequently the first step of drug development and is a significant starting point. Validating this step involves demonstrating that directing a compound toward the chosen target will lead to a beneficial therapeutic effect. This subsequently results in developing an assay to screen compounds and objectively identify their interaction with the target.
Once an assay has been established, a library of compounds is tested against the identified receptors, identifying new lead compounds. The biochemical interactions of newly developed drug compounds can be investigated on a molecular level by a variety of techniques, generally in vitro and in silico, prior to their administration in vivo.
If the mechanism of disease is poorly understood, then drug development is undirected, and it may be difficult to assess whether a drug is undergoing biochemical interactions that will ultimately halt disease progression.
However, even where the biochemistry of a drug is completely unknown, various PD indicators of drug activity can be monitored on a population level, revealing common responses. This is the purpose of population PK-PD modeling, allowing the typical response to drug treatment to be interpreted following administration to humans in clinical trials.
Population PK-PD Modeling of Neurodegenerative Diseases
Neurodegenerative diseases such as Alzheimer’s are currently poorly understood and are under intense scrutiny to reveal the complete mechanism of disease initiation and progression.
Alzheimer’s disease is known to start years and even decades before symptom onset; however, a lack of objective markers for this disease limits early diagnosis, despite being the best opportunity to eradicate onset and treat the disease. Various proteins have been noted to exert irregular activity during Alzheimer’s, such as β-amyloid and tau, and thus several drugs are based on antibodies to these proteins.
Owing to the disease site being within the brain, PK-PD modeling of these therapeutics must account for several additional compartments, such as rate of egress and ingress across the blood-brain barrier, and protein half-life, elimination rate, and degradation rate.
Direct measurement of protein concentration within the human brain is not possible, though it is achieved in animal models using probes and microdialysis apparatus, with fine capillaries placed within and around the brain to extract fluids. These methods have allowed models of protein activity in the brain and body and their influence on disease progression to be described. However, they are not yet useful as prognostic indicators in the clinic.
Non-invasive assessment of neurodegenerative disease progression in humans can be performed by comprehension examinations that aim to test the function of the brain, and various tests designed to provide a qualitative indicator of mental function are useful in determining treatment effectiveness. However, these methods fail to assist in revealing the biochemical activity taking place following drug administration and during disease progression, and thus only indirectly contribute to further refinement of drug design.
Future Outlook
Many drugs fail to attain FDA approval due to a poor understanding of the disease and activity of the drug on a physiological scale. Researchers and drug developers face many challenges, and a lack of understanding of the mechanisms behind diseases is a significant component that can lead to the development of unsafe or ineffective drugs and, ultimately wasted resources.
Understanding these diseases can include being more familiar with the biomarkers integral to disease onset, progression, and regression, leading to a more targeted approach that may result in more successful treatments for patients worldwide.
Sources
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- Sheng, L., Qu, Y., Yan, J., Liu, G.-Y., Wang, W.-L., Wang, Y.-J., Wang, H.-Y., Zhang, M.-Q., Lu, C., Liu, Y., Jia, J.-Y., Hu, C.-Y., Li, X.-N., Yu, C., & Xu, H.-R.. (2016). Population pharmacokinetic modeling and simulation of huperzine A in elderly Chinese subjects. Acta Pharmacologica Sinica, 37(7), 994–1001. https://doi.org/10.1038/aps.2016.24
- Bloomingdale, P., et al. (2022). PBPK-PD modeling for the preclinical development and clinical translation of tau antibodies for Alzheimer’s disease. Frontiers in Pharmacology, 13. https://doi.org/10.3389%2Ffphar.2022.867457
Further Reading