Cited by 242Statistical Methods in Clinical TrialsPharmaceutical studies and practicesPharmacogenetics and Drug Metabolism

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Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research

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Tuntland, Tove, Ethell, Brian, Kosaka, Takatoshi, Blasco, Francesca, Zang, Richard Xu, Jain, Monish, Gould, Ty, Hoffmaster, Keith·2014·Frontiers in Pharmacology

DOI 10.3389/fphar.2014.00174PMC PMC4112793

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Abstract

Characterizing the relationship between the pharmacokinetics (PK, concentration vs. time) and pharmacodynamics (PD, effect vs. time) is an important tool in the discovery and development of new drugs in the pharmaceutical industry. The purpose of this publication is to serve as a guide for drug discovery scientists toward optimal design and conduct of PK/PD studies in the research phase. This review is a result of the collaborative efforts of DMPK scientists from various Metabolism and Pharmacokinetic (MAP) departments of the global organization Novartis Institute of Biomedical Research (NIBR). We recommend that PK/PD strategies be implemented in early research phases of drug discovery projects to enable successful transition to drug development. Effective PK/PD study design, analysis, and interpretation can help scientists elucidate the relationship between PK and PD, understand the mechanism of drug action, and identify PK properties for further improvement and optimal compound design. Additionally, PK/PD modeling can help increase the translation of in vitro compound potency to the in vivo setting, reduce the number of in vivo animal studies, and improve translation of findings from preclinical species into the clinical setting. This review focuses on three important elements of successful PK/PD studies, namely partnership among key scientists involved in the study execution; parameters that influence study designs; and data analysis and interpretation. Specific examples and case studies are highlighted to help demonstrate key points for consideration. The intent is to provide a broad PK/PD foundation for colleagues in the pharmaceutical industry and serve as a tool to promote appropriate discussions on early research project teams with key scientists involved in PK/PD studies.

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Figure 1 The iterative process of PK/PD modeling in drug discovery.CC BY · PubMed Central

Figure 2 Progression from acute exploratory PK/PD studies to subchronic and chronic PK/PD studies as drug candidates are identified and profiled.CC BY · PubMed Central

Figure 3 PK and PD profiles of an oncology drug candidate in a mouse xenograph model. (A) Displays the pharmacokinetic profiles of a compound following oral administration at 30 and 100 mg/kg to mice. (B) Illustrates the PK/PD results at 100 mg/kg with the drug showing return of the PD response to baseline.CC BY · PubMed Central

Figure 4 Impact of non-specific binding on the total and unbound concentrations of a drug candidate in plasma and brain. (A) Shows the total drug concentrations in brain and plasma, while (B) shows the corresponding unbound drug concentrations. Both plasma protein and brain homogenate binding studies were conducted using the Rapid Equilibrium Dialysis (RED) device.CC BY · PubMed Central

Figure 5 Simulation of steady-state exposure and efficacy data of hypothetical dosing regimens in a preclinical mouse model. A drug was dosed at 30 mg/kg once daily (QD) or 3 mg/kg twice daily (BID) in a mouse xenograph model. Observed and predicted plasma levels were plotted with the simulated PD responses to aid selection of dose and dosing frequency in a follow-up efficacy study.CC BY · PubMed Central

Figure 6 Dose fractionation studies to determine the pharmacokinetic driver. Impact of once daily (QD) vs. twice daily (BID) dosing regimens of the same total daily dose; both doses yield identical overall AUC0-24 h values but different Cmax concentrations over the course of the dosing regimens.CC BY · PubMed Central

Figure 7 Dose dependent PK and PD observed in a rat model of diabetes. (A) Shows the PK with time, (B) shows the PD with time, and (C) plot of PK vs. PD. There was instantaneous equilibrium between exposure and effect, thus the PK/PD data were modeled using a direct Sigmoidal Emax response model.CC BY · PubMed Central

Figure 8 Effect vs. plasma concentration of a drug candidate following a single oral dose to tumor bearing mice. (A) Shows the PK and PD response both plotted against time. (B) Shows the PK plotted directly against PD. Delayed pharmacodynamic response resulted in a hysteris plot. An indirect response model (inhibition of input function) was subsequently developed to describe and predict PD response inhibition based upon various dosing regimens.CC BY · PubMed Central

Figure 9 Total plasma and liver exposures relative to pharmacodynamics response. Rat total plasma and target tissue (liver) exposure was plotted with PD effect. A sigmoidal Emax model with baseline and Hill coefficient (γ) correction factors was used to describe the dataset. Improved in vitro-in vivo correlation was obtained when converting total plasma concentration to free plasma concentration for the estimate of in vivo EC50.CC BY · PubMed Central

Figure 10 Biological cascade of BRAFV600E activation and cell cycle implications. The BRAFV600E pathway includes multiple biomarkers whose direct or indirect response could be indicative of efficacy.CC BY · PubMed Central

Figure 11 Using multiple biomarkers to indicate modulation of the BRAFV600E pathway. A single oral dose was administered to tumor bearing mice. Plasma and tumor tissue were collected from the same mice to simultaneously obtain plasma PK and biomarker response of phospho-MEK and phospho-ERK in tumors.CC BY · PubMed Central

Figure 12 Variability in PK and PD data. Dose-dependent suppression of biomarker response in rats following administration of a drug candidate. The error bars represent variability in both plasma AUC and biomarker response.CC BY · PubMed Central

Figure 13 Extrapolating preclinical PK/PD data to the clinic.CC BY · PubMed Central

Figure 14 Translation of preclinical PD data to humans. (A) Illustrates the ex vivo incubation data of a drug molecule in whole blood from Cynomolgus monkey when used to determine concentration dependent changes in the desired PD effect. (B) Shows how the model fit of PD parameters from the incubations were used to predict the in vivo efficacy. Allometric scaling of PK parameters enabled simulations of plasma concentrations in humans.CC BY · PubMed Central

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HSF1 Pathway Inhibitor Clinical Candidate (CCT361814/NXP800) Developed from a Phenotypic Screen as a Potential Treatment for Refractory Ovarian Cancer and Other Malignancies
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“50–500 1 15 4 3 5 (2) 14 (13) 2 (6) 2 (2) 7 (7) 11 (10) 3 (4) 2 (1)”
Plasma Protein Binding of Highly Bound Drugs Determined With Equilibrium Gel Filtration of Nonradiolabeled Compounds and LC-MS/MS Detection
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“To attain a desirable in vivo response, there should be an equilibrium between pharmacokinetic and pharmacodynamic qualities (Tuntland et al., 2014).”
Background
Cumulative Fraction of Response for Once- and Twice-Daily Delamanid in Patients with Pulmonary Multidrug-Resistant Tuberculosis
Mallikaarjun, S., Chapagain, M. L., Sasaki, T., Hariguchi, N., Deshpande, D., Srivastava, S., Berg, A. K., Hirota, K., Inoue, Y., Matsumoto, M., et al. (2020) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.01207-20 · Cited by 24
Advances in reversible covalent kinase inhibitors
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Strategic Priorities for Research on Antibiotic Alternatives in Animal Agriculture—Results From an Expert Workshop
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Optimizing Antimicrobial Therapy by Integrating Multi-Omics With Pharmacokinetic/Pharmacodynamic Models and Precision Dosing
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Combined Bioinformatics and Combinatorial Chemistry Tools to Locate Drug‐Able Anti‐TB Phytochemicals: A Cost‐Effective Platform for Natural Product‐Based Drug Discovery
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“31] The pharmacokinetics and ADMET properties of all selected candidates were recorded ( Table S4 and Figure 2 ).”
Background
Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies
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Mitochondrial Imbalance of Trypanosoma cruzi Induced by the Marine Alkaloid 6-Bromo-2′-de-N-Methylaplysinopsin
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“Therefore, Drug metabolism and pharmacokinetic (DMPK) properties are crucial in the drug research process (Hsieh et al., 2009; Tuntland et al., 2014; Singh et al., 2006; Yengi et al., 2007; Thompson et al.,2000; Caldwell et al.,2001; Alavijeh et al.,2004).”
Background
Comparative physiology and efficacy of atropine and scopolamine in sarin nerve agent poisoning
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Evaluation of pharmacokinetic‐pharmacodynamic relationships and selection of drug combinations for tuberculosis
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“Moreover, it can be anticipated that extrapolations including PK and PD variability will further enhance the quality of decisions regarding the ranking and selection of the companion drugs for combination therapy.(42)”
Background
Pharmacokinetics and population pharmacokinetics in pediatric oncology
Sassen, S. D. T., Zwaan, C. M., van der Sluis, I. M., & Mathôt, R. A. A. (2019) · Pediatric Blood & Cancer · doi:10.1002/pbc.28132 · Cited by 19
PK/PD Mediated Dose Optimization of Emactuzumab, a CSF1R Inhibitor, in Patients With Advanced Solid Tumors and Diffuse‐Type Tenosynovial Giant Cell Tumor
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“10,12,13 Such strategies can provide quantitative data to guide dose selection for early phase trials.”
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“ing methods can be particularly valuable (14,15).”
Population Pharmacokinetic‐Pharmacodynamic Modeling for Propofol Anesthesia Guided by the Bispectral Index (BIS)
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Artificial Intelligence: Future Aspects in the Pharmaceutical Industry an Overview
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“To avoid the formation of necrotic cores by exceeding diameter of 500 μm (Vinci et al., 2012), spheroids were used at an initial diameter of 200–250 μm, and they did not exceed 400 μm in diameter for the duration of our experiments.”
Method
Comparison of metabolic pathways of different α-N-heterocyclic thiosemicarbazones
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“This highlights the importance of early metabolic studies in the drug optimization process in order to select the most promising candidate(s) for further evaluation [3].”
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Relationship Between Anifrolumab Pharmacokinetics, Pharmacodynamics, and Efficacy in Patients With Moderate to Severe Systemic Lupus Erythematosus
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The Pharmacokinetic–Pharmacodynamic (PKPD) Relationships of AZD3229, a Novel and Selective Inhibitor of KIT, in a Range of Mouse Xenograft Models of GIST
Reddy, V. P., Anjum, R., Grondine, M. R., Smith, A., Bhavsar, D., Barry, E., Guichard, S. M., Shao, W., Kettle, J. G., Brown, C., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-2848 · Cited by 15
“Pharmacokinetic–pharmacodynamic (PKPD) modeling has emerged as an important capability in drug discovery and development to assist in the design of studies and the integration and analysis of datasets to quantitatively investigate the understanding of drug action (15).”
Background
Meta-analysis of material properties influencing nanoparticle plasma pharmacokinetics
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New thiazole, thiophene and 2-pyridone compounds incorporating dimethylaniline moiety: synthesis, cytotoxicity, ADME and molecular docking studies
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Pharmacokinetics and disposition of miltefosine in healthy mice and hamsters experimentally infected with Leishmania infantum
Jiménez-Antón, M. D., García‐Calvo, E., Gutiérrez, C., Escribano, M. D., Kayali, N., Luque-Garcia, J. L., Olías-Molero, A. I., Corral, M. J., Costi, M. P., Torrado, J. J., et al. (2018) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2018.06.002 · Cited by 14
Microfluidic technologies for local drug delivery
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Combined Microdialysis-Tumor Homogenate Method for the Study of the Steady State Compartmental Distribution of a Hydrophobic Anticancer Drug in Patient-Derived Xenografts
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“This approach requires a large number of animals to provide a complete concentration-time profile of drug distribution in the tumor (7).”
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Wang, L., Huang, X., Sun, M., Zheng, T., Zheng, L., Lin, X., Ruan, J., & Lin, F. Y. (2024) · Nutrition and Diabetes · doi:10.1038/s41387-024-00262-w · Cited by 12
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Correlation between anti-PD-L1 tumor concentrations and tumor-specific and nonspecific biomarkers in a melanoma mouse model
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Upholding or Breaking the Law of Superposition in Pharmacokinetics
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Authors: Tuntland, T., Ethell, B., Kosaka, T., Blasco, F., Zang, R. X., Jain, M., Gould, T., & Hoffmaster, K.
Affiliations: Genomics Institute of the Novartis Research Foundation (US)
Novartis (Singapore) (SG)
Corresponding author: Tuntland, Tove
ORCID: Tuntland, Tove
Zang, Richard Xu
Venue: Frontiers in Pharmacology
Publisher: Frontiers Media SA
Published: 2014-07-28
Volume: 5
Pages: 174-174
ISSN: 1663-9812 (ISSN-L)
DOI: 10.3389/fphar.2014.00174
PMC: PMC4112793
OpenAlex: W2070719925
Cited by: 239
References: 70
Citations by year: 2026: 23 · 2025: 25 · 2024: 26 · 2023: 25 · 2022: 24 · 2021: 25 · 2020: 28 · 2019: 16 · 2018: 11 · 2017: 17 · 2016: 10 · 2015: 8
FWCI: 1.48
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: Frontiers in Pharmacology (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
Keywords: pharmacokinetics, pharmacodynamics, pharmacokinetic-pharmacodynamic modeling, drug discovery, research design, data analysis and interpretation, mechanism of drug action, in vitro to in vivo translation, preclinical to clinical translation, compound design, inter-agency cooperation

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Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research

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