Drugs and Endogenous Ligands Compete for Receptor Occupancy

Colburn, Wayne A.

doi:10.1002/j.1552-4604.1994.tb04724.x

Cited by 9 publications

(10 citation statements)

References 43 publications

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“…Endogenous ligands compete with drugs for receptor occupancy and can thereby influence therapeutic drug intervention 19 . This competition is consistent with certain protein biomarker changes observed between health and disease.…”

Section: Mechanism‐based Biomarkerssupporting

confidence: 71%

“…In addition, this observation is consistent with dosing of an enzyme inducing or inhibiting agent‐altering metabolic processes that result in increased or decreased concentrations of a substrate. For example, it has been shown that antibodies or biotechnology‐derived receptors (etanercept) that bind endogenous ligands such as TNF‐α reduce ligand concentrations that may be found in excess in certain disease states and thereby cause the disease to revert to a more healthy state 19 . These examples support using changes in biochemical/molecular marker concentrations as one way to monitor the effects of disease treatment (Figure 3).…”

Section: Mechanism‐based Biomarkersmentioning

confidence: 95%

“…In segment c, administration of exogenous antibody or receptor reestablishes the healthy half‐filled balance. Adapted from Colburn 19 …”

Section: Mechanism‐based Biomarkersmentioning

confidence: 99%

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Biomarkers in Drug Discovery and Development: From Target Identification through Drug Marketing

Colburn¹

2003

The Journal of Clinical Pharma

Self Cite

138

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Biomarkers of disease play an important role in medicine and have begun to assume a greater role in drug discovery and development. The challenge for biomarkers is to allow earlier, more robust drug safety and efficacy measurements. Their role in drug development will continue to grow for the foreseeable future. For biomarkers to assume their rightful role, greater understanding of the mechanism of disease progression and therapeutic intervention is needed. In addition, greater understanding of the requirements for biomarker selection and validation, biomarker assay method validation and application, and clinical endpoint validation and application is needed. Biomarkers need to be taken into account while the therapeutic target is still being identified and the concept is being formulated. Biomarkers need to be incorporated into a continuous cycle that takes what is learned from the discovery and development of one series of biomarkers and translates it into the next series of biomarkers. Optimum biomarker development and application will require a team approach because of the multifaceted nature of biomarker selection, validation, and application, using such techniques as pharmacoepidemiology, pharmacogenetics, pharmacogenomics, and functional proteomics; bioanalytical method development and validation; disease process and therapeutic intervention assessments; and pharmacokinetic/pharmacodynamic modeling and simulation to improve and refine drug development. The potential for biomarkers in medicine and drug development will be limited by the least effective component of the processes. The team approach will minimize the potential for the least effective component to be fatal to the rest of the process. As scientific/regulatory foundations for biomarkers in medicine and drug development begin to be established, successes and applications will need to be effectively communicated with all of the stakeholders, including not only internal and external drug developers and regulators but also the medical community, to ensure that biomarkers are totally integrated into drug discovery and development as well as the practice of medicine.

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Section: Mechanism‐based Biomarkerssupporting

confidence: 71%

Section: Mechanism‐based Biomarkersmentioning

confidence: 95%

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Biomarkers in Drug Discovery and Development: From Target Identification through Drug Marketing

Colburn¹

2003

The Journal of Clinical Pharma

Self Cite

138

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“…Two hidden node networks, as in the case of link models, were used. Training was performed with time, dose (5,10,20), and concentration of the drug in the biofluid as inputs and the indirect response as the target. The training outcome is presented in Figure 4 (panel A) at the three doses.…”

Section: Resultsmentioning

confidence: 99%

“…Whether endogenous substances compete with the active molecule(s) or not is unknown. [18][19][20] Applying conventional techniques in such cases would require major assumptions that may not be accurate. Ethical and regulatory issues prevent the investigation of the in vivo characteristics of the biotransformation product separately.…”

Section: Resultsmentioning

confidence: 99%

Artificial Neural Networks As a Novel Approach to Integrated Pharmacokinetic—Pharmacodynamic Analysis

Gobburu

Chen

1996

Journal of Pharmaceutical Sciences

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A novel model-independent approach to analyze pharmacokinetic (PK)-pharmacodynamic (PD) data using artificial neural networks (ANNs) is presented. ANNs are versatile computational tools that possess the attributes of adaptive learning and self-organization. The emulative ability of neural networks is evaluated with simulated PK-PD data, and the power of ANNs to extrapolate the acquired knowledge is investigated. ANNs of one architecture are shown to be flexible enough to accurately predict PD profiles for a wide variety of PK-PD relationships (e.g., effect compartment linked to the central or peripheral compartment and indirect response models). Also, an example is given of the ability of ANNs to accurately predict PD profiles without requiring any information regarding the active metabolite. Because structural details are not required, ANNs exhibit a clear advantage over conventional model-dependent methods. ANNs are proved to be robust toward error in the data and perturbations in the initial estimates. Moreover, ANNs were shown to handle sparse data well. Neural networks are emerging as promising tools in the field of drug discovery and development.

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Mechanism and Physiologically Based PK / PD Model in Assisting Translation from Preclinical to Clinical: Understanding PK / PD of Therapeutic Proteins at Site‐of‐Action

Chen

Wang

2019

Quantitative Pharmacology and Individualized Therapy Strategies in Development of Therapeutic Proteins for Immune‐Mediated Infl

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Drugs and Endogenous Ligands Compete for Receptor Occupancy

Cited by 9 publications

References 43 publications

Biomarkers in Drug Discovery and Development: From Target Identification through Drug Marketing

Biomarkers in Drug Discovery and Development: From Target Identification through Drug Marketing

Artificial Neural Networks As a Novel Approach to Integrated Pharmacokinetic—Pharmacodynamic Analysis

Mechanism and Physiologically Based PK / PD Model in Assisting Translation from Preclinical to Clinical: Understanding PK / PD of Therapeutic Proteins at Site‐of‐Action

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