OPEN-ACCESS PEER-REVIEWED

1Dr. Itishree Prusty, 2Dr. Sailen Kumar Mishra, 3Saswat Satapathy, 4Dr. Yajnesh Prasanna Sahu, 5Harmohan Singh Yadav, 6Dr. Dinesh Kumar Mishra

1*Assistant Professor, Department of Pharmacology, SCB Medical College,Cuttack, India.

2Assistant Professor Pharmacology, Shri Jagannath Medical College & Hospital, Puri, Odisha, India.

3Assistant Professor, Department of Pharmacology,Saheed Rendo Majhi Medical College,Kalahandi, India.

4Assistant Professor, Department of Pharmacology, Bhima Bhoi Medical College, Balangir, Odisha, India.

5Associate Professor, Department of Biochemistry, Rama Medical College Hospital & Research Centre, Mandhana, Kanpur (U.P) -209 217. India.

6Associate Professor, Department of Chemistry, AKS University Satna,Madhya Pradesh, India.

Download PDF

Abstract

The field of drug discovery and development is constantly evolving due to advancements in pharmacokinetics and pharmacodynamics research. The abstract explores the latest insights and methodologies influencing the critical aspects of drug development. Pharmacokinetics, which includes absorption, distribution, metabolism, and excretion (ADME), is crucial for evaluating the safety and efficacy of medications.Nanotechnology and targeted drug delivery are revolutionizing the interaction between drugs and biological systems. Pharmacodynamics studies integrate drugs and molecular targets to understand their interactions, revealing efficacy, potency, and potential adverse effects. The article discusses recent advancements in comprehending drug-receptor interactions, signaling pathways, and therapeutic response variability. Emerging technologies like computational modeling and high-throughput screening are accelerating the discovery of new drug candidates. This explores new drug discovery and development avenues, contributing to ongoing dialogue on optimizing therapeutic interventions and translating basic research into clinical applications.With an emphasis on the complex mechanisms regulating therapeutic efficacy and safety, this abstract examines the significance of pharmacokinetics and pharmacodynamics in drug discovery and development. It highlights recent advancements in drug delivery systems, such as nanoparticle-based platforms, and the use of cutting-edge methodologies like computational modeling and high-throughput screening. The aim is to develop safer, more effective therapeutics and advance personalized medicine.

Keywords: Drug discovery, Pharmacokinetics, Pharmacodynamics, Target, drug delivery systems, biological systems

References

[1]. Alampanos, V., Samanidou, V., & Papadoyannis, I. (2019). Trends in Sample Preparation for the HPLC Determination of Penicillins in Biofluids. Journal of Applied Bioanalysis, 5(1), 9–17.
[2]. Agoram, B. M., Martin, S. W., Van der Graaf, P. H. 2007. The role of mechanism-based pharmacokinetic–pharmacodynamic (PK–PD) modeling in translational research of biologics. Drug Discovery Today, 12(23-24), 1018-1024.
[3]. Andersson, T. B., Bredberg, E., Ericsson, H., Sjöberg, H. 2004. An evaluation of the in vitro metabolism data for predicting the clearance and drug-drug interaction potential of CYP2C9 substrates. Drug Metabolism and Disposition, 32(7), 715-721.
[4]. Ashford, J. W., Schmitt, F. A. 2001. Modeling the time-course of Alzheimer’s dementia. Current Psychiatry Reports, 3(1), 20-28.
[5]. Azad, A. K., Praveen, M., & Sulaiman, W. M. A. B. W. (2024). Assessment of Anticancer Properties of Plumbago zeylanica. Harnessing Medicinal Plants in Cancer Prevention and Treatment, 91–121. https://doi.org/10.4018/979-8-3693-1646-7.ch004
[6]. Beier, H., Garrido, M. J., Christoph, T., et al. 2008. Semi-mechanistic pharmacokinetic/pharmacodynamic modelling of the antinociceptive response in the presence of competitive antagonism. Pharmaceutical research, 25(8), 1789–1797.
[7]. https://doi.org/10.1007/s11095-007-9489-8.
[8]. Bender, G., Gosset, J., Florian, J., et al. 2009. Population pharmacokinetic model of the pregabalin-sildenafil interaction in rats: application of simulation to preclinical PK-PD study design. Pharmaceutical research, 26(10), 2259–2269. https://doi.org/10.1007/s11095-009-9942-y.
[9]. Berdigaliyev, N., Aljofan, M. 2020. An overview of drug discovery and development. Future Medicinal Chemistry, 12(10), 939–947.
[10]. Bergen, A. A., Kaing, S., ten Brink, J. B., et al. 2015. Gene expression and functional annotation of human choroid plexus epithelium failure in Alzheimer’s disease. BMC genomics, 16, 956. https://doi.org/10.1186/s12864-015-2159-zBettler,
[11]. Bhavnani, S. M., Krause, K. M., Ambrose, P. G. 2020. A broken antibiotic market: Review of strategies to incentivize drug development. Open Forum Infectious Diseases, 7(7), ofaa083.
[12]. Blair, J. A., Rauh, D., Kung, C., et al. 2007. Structure-guided development of affinity probes for tyrosine kinases using chemical genetics. Nature chemical biology, 3(4), 229–238. https://doi.org/10.1038/nchembio866
[13]. Block, M. 2015. Physiologically based pharmacokinetic and pharmacodynamic modeling in cancer drug development: status, potential and gaps. Expert Opinion on Drug Metabolism & Toxicology, 11(5), 743-756.
[14]. Boxenbaum, H. 1982. Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. Journal of Pharmacokinetics and Biopharmaceutics, 10, 201-227.
[15]. Burt, T., Young, G., Lee, W., et al. 2020. Phase 0/microdosing approaches: time for mainstream application in drug develodevelopment? e reviews. Drug discovery, 19(11), 801–818. https://doi.org/10.1038/s41573-020-0080-x
[16]. Calipari, E. S., Ferris, M. J. 2013. Amphetamine mechanisms and actions at the dopamine terminal revisited. Journal of Neuroscience, 33(21), 8923-8925.
[17]. Chiba, M., Ishii, Y., Sugiyama, Y. 2009. Prediction of hepatic clearance in humans from in vitro data for successful drug development. The AAPS Journal, 11, 262-276.
[18]. Clement, P., Mutsaerts, H. J., Václavů, L., et al. 2018. Variability of physiological brain perfusion in healthy subjects – A systematic review of modifiers. International Society of Cerebral Blood Flow and Metabolism, 38(9), 1418–1437. https://doi.org/10.1177/0271678X17702156
[19]. Danhof, M., Alvan, G., Dahl, S. G., et al. 2005. Mechanism-based pharmacokinetic-pharmacodynamic modeling-a new classification of biomarkers. Pharmaceutical research, 22(9), 1432–1437.
[20]. https://doi.org/10.1007/s11095-005-5882-3
[21]. Danhof, M., de Jongh, J., De Lange, E. C., et al. 2007. Mechanism-based pharmacokinetic-pharmacodynamic modeling: biophase distribution, receptor theory, and dynamical systems analysis. Annual Review of Pharmacology and Toxicology, 47, 357-400.
[22]. Danhof, M., De Lange, E. C., Della Pasqua, O. E., et al. 2008. Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research. Trends in Pharmacological Sciences, 29(4), 186-191.
[23]. Daryaee, F., Tonge, P. J. 2017. A quantitative mechanistic PK/PD model directly connects Btk target engagement and in vivo efficacy. Chemical Science, 8(5), 3434–3443.
[24]. Daryaee, F., Tonge, P. J. 2019. Pharmacokinetic–pharmacodynamic models that incorporate drug–target binding kinetics. Current Opinion in Chemical Biology, 50, 120–127.
[25]. Daryaee, F., Chang, A., Schiebel, J., et al. 2016. Correlating Drug-Target Kinetics and In vivo Pharmacodynamics: Long Residence Time Inhibitors of the FabI Enoyl-ACP Reductase. Chemical science, 7(9), 5945–5954. https://doi.org/10.1039/C6SC01000H.
[26]. de Lange, E. C. 2013. The mastermind approach to CNS drug therapy: translational prediction of human brain distribution, target site kinetics, and therapeutic effects. Fluids and Barriers of the CNS, 10, 1-16.
[27]. de Lange, E. C. 2015. PBPK Modeling Approach for Predictions of Human CNS Drug Brain Distribution. Blood‐Brain Barrier in Drug Discovery, 296–323. https://doi.org/10.1002/9781118788523.ch14
[28]. de Lange, E. C., Ravenstijn, P. G., Groenendaal, D., van Steeg, T. J. 2005. Toward the prediction of CNS drug-effect profiles in physiological and pathological conditions using microdialysis and mechanism-based pharmacokinetic-pharmacodynamic modeling. The AAPS Journal, 7, E532-E543.
[29]. de Witte, W. E., Danhof, M., van der Graaf, P. H., de Lange, E. C. 2016. In vivo target residence time and kinetic selectivity: The association rate constant as determinant. Trends in Pharmacological Sciences, 37(10), 831-842.
[30]. de Witte, W. E., Wong, Y. C., Nederpelt, I., et al. 2016. Mechanistic models enable the rational use of in vitro drug-target binding kinetics for better drug effects in patients. Expert opinion on drug discovery, 11(1), 45–63. https://doi.org/10.1517/17460441.2016.1100163
[31]. Di Paolo, J. A., Huang, T., Balazs, M., et al. 2011. Specific Btk inhibition suppresses B cell- and myeloid cell-mediated arthritis. Nature chemical biology, 7(1), 41–50. https://doi.org/10.1038/nchembio.481
[32]. Di, L., Feng, B., Goosen, T. C., et al .2013. A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development. Drug metabolism and disposition: the biological fate of chemicals, 41(12), 1975–1993. https://doi.org/10.1124/dmd.113.054031
[33]. Dokoumetzidis, A., Iliadis, A., Macheras, P. 2002. Nonlinear dynamics in clinical pharmacology: the paradigm of cortisol secretion and suppression. British Journal of Clinical Pharmacology, 54(1), 21.
[34]. Dong, J. Q., Chen, B., Gibbs, M. A., et al. 2008. Applications of computer-aided pharmacokinetic and pharmacodynamic methods from drug discovery through registration. Current Computer-Aided Drug Design, 4 (1), 54-66.
[35]. Drexler, D. M., McNaney, C. A., Wang, Y., Huang, X., & Reily, M. D. (2018). The utility of qNMR to improve accuracy and precision of LC-MS bioanalysis. Journal of Applied Bioanalysis, 4(1), 26–31.
[36]. Drusano, G. L. (2004). Antimicrobial pharmacodynamics: Critical interactions of ‘bug and drug’. Nature Reviews Microbiology, 2(4), 289-300.
[37]. Erdő, F., Denes, L., de Lange, E. 2017. Age-associated physiological and pathological changes at the blood–brain barrier: A review. Journal of Cerebral Blood Flow & Metabolism, 37(1), 4-24.
[38]. Evans, E. K., Tester, R., Aslanian, S., et al. 2013. Inhibition of Btk with CC-292 provides early pharmacodynamic assessment of activity in mice and humans. The Journal of pharmacology and experimental therapeutics, 346(2), 219–228. https://doi.org/10.1124/jpet.113.203489.
[39]. Fakler, B. 2017. Ionotropic AMPA-type glutamate and metabotropic GABAB receptors: Determining cellular physiology by proteomes. Current Opinion in Neurobiology, 45, 16-23.
[40]. Faria, M., Ismaiel, O., Waltrip, J., Mariannino, T., Yuan, M., Mylott, W., Roongta, V., Shen, J., & Kadiya, P. (2020). LC-MS/MS Method for the Quantitative Determination of Tanespimycin and its Active Metabolite in Human Plasma: Method Validation and Overcoming an Insidious APCI Source Phenomenon. Journal of Applied Bioanalysis, 6(3), 145–163.
[41]. Finnema, S. J., Scheinin, M., Shahid, M., et al. 2015. Application of cross-species PET imaging to assess neurotransmitter release in brain. Psychopharmacology, 232(21-22), 4129–4157. https://doi.org/10.1007/s00213-015-3938-6.
[42]. Fridén, M., Winiwarter, S., Jerndal, G., et al. 2009. Structure-brain exposure relationships in rat and human using a novel data set of unbound drug concentrations in brain interstitial and cerebrospinal fluids. Journal of medicinal chemistry, 52(20), 6233–6243. https://doi.org/10.1021/jm901036q.
[43]. Gabrielsson, J., Dolgos, H., Gillberg, P.-G., et al. 2009. Early integration of pharmacokinetic and dynamic reasoning is essential for optimal development of lead compounds: strategic considerations. Drug Discovery Today, 14(7-8), 358-372.
[44]. Gabrielsson, J., Hjorth, S. 2016. Pattern recognition in pharmacodynamic data analysis. The AAPS Journal, 18, 64-91.
[45]. Gabrielsson, J., Peletier, L. A. 2014. Dose–response–time data analysis involving nonlinear dynamics, feedback and delay. European Journal of Pharmaceutical Sciences, 59, 36-48.
[46]. Gabrielsson, J., Peletier, L. A., Hjorth, S. 2018. In vivo potency revisited–keep the target in sight. Pharmacology & Therapeutics, 184, 177-188.
[47]. Gabrielsson, J., Weiner, D. 2001. Pharmacokinetic and pharmacodynamic data analysis: concepts and applications.
[48]. Gao, W., Jusko, W. J. 2012. Target-mediated pharmacokinetic and pharmacodynamic model of exendin-4 in rats, monkeys, and humans. Drug Metabolism and Disposition, 40(5), 990-997.
[49]. Gaugler, S., Rykl, J., Grill, M., & Cebolla, V. L. (2018). Fully automated drug screening of dried blood spots using online LC-MS/MS analysis. Journal of Applied Bioanalysis, 4(1), 7–15.
[50]. Gibiansky, L., Gibiansky, E. 2009. Target-mediated drug disposition model: approximations, identifiability of model parameters and applications to the population pharmacokinetic–pharmacodynamic modeling of biologics. Expert Opinion on Drug Metabolism & Toxicology, 5(7), 803-812.
[51]. Groenendaal, D., Freijer, J., Rosier, A., et al. 2008. Pharmacokinetic/pharmacodynamic modelling of the EEG effects of opioids: the role of complex biophase distribution kinetics. European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences, 34(2-3), 149–163. https://doi.org/10.1016/j.ejps.2008.03.003.
[52]. Groeneveld, G. J., Hay, J. L.,Van Gerven, J. M. 2016. Measuring blood–brain barrier penetration using the NeuroCart, a CNS test battery. Drug Discovery Today: Technologies, 20, 27-34.
[53]. Grover, A. K. 2013. Use of allosteric targets in the discovery of safer drugs. Medical Principles and Practice, 22(5), 418–426.
[54]. Harrold, J., Ramanathan, M., Mager, D. 2013. Network-based approaches in drug discovery and early development. Clinical Pharmacology & Therapeutics, 94(6), 651–658.
[55]. Holford, N., Nutt, J. G. 2008. Disease progression, drug action and Parkinson’s disease: Why time cannot be ignored. European Journal of Clinical Pharmacology, 64, 207-216.
[56]. Honigberg, L. A., Smith, A. M., Sirisawad, M., et al. 2010. The Bruton tyrosine kinase inhibitor PCI-32765 blocks B-cell activation and is efficacious in models of autoimmune disease and B-cell malignancy. Proceedings of the National Academy of Sciences of the United States of America, 107(29), 13075–13080. https://doi.org/10.1073/pnas.1004594107
[57]. Hopkins, A. L. 2008. Network pharmacology: The next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682–690.
[58]. Houston, J. B., Galetin, A. 2008. Methods for predicting in vivo pharmacokinetics using data from in vitro assays. Current Drug Metabolism, 9(9), 940-951.
[59]. Hung, C. L., Chen, C. C. 2014. Computational approaches for drug discovery. Drug Development Research, 75(6), 412–418.
[60]. Huntley, M. A., Bien-Ly, N., Daneman, R., Watts, R. J. 2014. Dissecting gene expression at the blood-brain barrier. Frontiers in Neuroscience, 8, 355.
[61]. Ito, K., Houston, J. B. 2004. Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharmaceutical Research, 21, 785-792.
[62]. Iwatsubo, T., Hirota, N., Ooie, T., et al. 1997. Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacology & therapeutics, 73(2), 147–171. https://doi.org/10.1016/s0163-7258(96)00184-2
[63]. Jonsson, E. N., Macintyre, F., James, I., et al. 2005. Bridging the pharmacokinetics and pharmacodynamics of UK-279,276 across healthy volunteers and stroke patients using a mechanistically based model for target-mediated disposition. Pharmaceutical research, 22(8), 1236–1246. https://doi.org/10.1007/s11095-005-5264-x
[64]. Jumbe, N. L., Xin, Y., Leipold, D. D., et al. 2010. Modeling the efficacy of trastuzumab-DM1, an antibody drug conjugate, in mice. Journal of pharmacokinetics and pharmacodynamics, 37(3), 221–242. https://doi.org/10.1007/s10928-010-9156-2
[65]. Kaddurah-Daouk, R., Kristal, B. S., Weinshilboum, R. M. 2008. Metabolomics: a global biochemical approach to drug response and disease. Annual Review of Pharmacology and Toxicology, 48, 653-683.
[66]. Katewa, A., Wang, Y., Hackney, J. A., et al. 2017. Btk-specific inhibition blocks pathogenic plasma cell signatures and myeloid cell-associated damage in IFNα-driven lupus nephritis. JCI insight, 2(7), e90111. https://doi.org/10.1172/jci.insight.90111
[67]. Kraus, V. B. 2018. Biomarkers as drug development tools: Discovery, validation, qualification and use. Nature Reviews Rheumatology, 14(6), 354-362.
[68]. Kummar, S., Kinders, R., Rubinstein, L., et al. 2007. Compressing drug development timelines in oncology using phase ‘0’ trials. Nature reviews. Cancer, 7(2), 131–139. https://doi.org/10.1038/nrc2066.
[69]. Lalonde, R. L., Kowalski, K. G., Hutmacher, M. M., et al. 2007. Model-based drug development. Clinical pharmacology and therapeutics, 82(1), 21–32. https://doi.org/10.1038/sj.clpt.6100235
[70]. Lappin, G., Noveck, R., Burt, T. 2013. Microdosing and drug development: past, present and future. Expert Opinion on Drug Metabolism & Toxicology, 9(7), 817-834.
[71]. Lavecchia, A. 2015. Machine-learning approaches in drug discovery: Methods and applications. Drug Discovery Today, 20(3), 318–331.
[72]. Lessl, M., Bryans, J. S., Richards, D., Asadullah, K. 2011. Crowd sourcing in drug discovery. Nature Reviews Drug Discovery, 10(4), 241–242.
[73]. Liu, H., Zhang, H., IJzerman, A. P., Guo, D. 2023. The translational value of ligand-receptor binding kinetics in drug discovery. British journal of pharmacology, 10.1111/bph.16241. Advance online publication. https://doi.org/10.1111/bph.16241
[74]. Liu, S., Cai, W., Liu, S., et al. 2015. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain informatics, 2(3), 167–180. https://doi.org/10.1007/s40708-015-0019-x
[75]. Lobo, E. D., Hansen, R. J., Balthasar, J. P. 2004. Antibody pharmacokinetics and pharmacodynamics. Journal of Pharmaceutical Sciences, 93(11), 2645-2668.
[76]. LoRusso, P. M. 2009. Phase 0 clinical trials: an answer to drug development stagnation? Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 27(16), 2586-2588.
[77]. Marchetti, S., Schellens, J. 2007. The impact of FDA and EMEA guidelines on drug development in relation to Phase 0 trials. British Journal of Cancer, 97(5), 577-581.
[78]. Margineanu, D. G. 2016. Neuropharmacology beyond reductionism–a likely prospect. Biosystems, 141, 1-9.
[79]. Martinez-Gamboa, L., Brezinschek, H. P., Burmester, G. R., Dörner, T. 2006. Immunopathologic role of B lymphocytes in rheumatoid arthritis: rationale of B cell-directed therapy. Autoimmunity reviews, 5(7), 437–442. https://doi.org/10.1016/j.autrev.2006.02.004.
[80]. Mayer, B., Heinzel, A., Lukas, A., Perco, P. 2017. Predictive biomarkers for linking disease pathology and drug effect. Current Pharmaceutical Design, 23(1), 29-54.
[81]. Meibohm, B., Derendorf, H. 2004. Pharmacokinetics and pharmacodynamics of biotech drugs. In Pharmaceutical biotechnology: Drug discovery and clinical applications (pp. 145-172).
[82]. Miller, R., Ewy, W., Corrigan, B. W., et al. 2005. How Modeling and Simulation Have Enhanced Decision Making in New Drug Development. Journal of Pharmacokinetics and Pharmacodynamics, 32(2), 185–197. https://doi.org/10.1007/s10928-005-0074-7
[83]. Morgan, P., Van Der Graaf, P. H., Arrowsmith, J., et al. 2012. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug discovery today, 17(9-10), 419–424. https://doi.org/10.1016/j.drudis.2011.12.020.
[84]. Morrow, T., Felcone, L. H. 2004. Defining the difference: What makes biologics unique. Biotechnology Healthcare, 1(4), 24.
[85]. Müller, U. C., Deller, T., Korte, M. 2017. Not just amyloid: Physiological functions of the amyloid precursor protein family. Nature Reviews Neuroscience, 18(5), 281-298.
[86]. Nishimura, Y., Tagawa, M., Ito, H., Tsuruma, K., & Hara, H. (2017). Overcoming Obstacles to Drug Repositioning in Japan. Frontiers in pharmacology, 8, 729. https://doi.org/10.3389/fphar.2017.00729
[87]. Obach, R. S., Baxter, J. G., Liston, T. E., et al. 1997. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. The Journal of pharmacology and experimental therapeutics, 283(1), 46–58.
[88]. Orloff, J. J., Stanski, D. 2011. Innovative approaches to clinical development and trial design. Annali dell’Istituto Superiore di Sanità, 47, 8–13.
[89]. Overgaard, R. V., Holford, N., Rytved, K. A., Madsen, H. 2007. Pkpd model of interleukin-21 effects on thermoregulation in monkeys—application and evaluation of stochastic differential equations. Pharmaceutical Research, 24, 298-309.
[90]. Praveen, M. (2024). Characterizing the West Nile Virus’s polyprotein from nucleotide sequence to protein structure – Computational tools. J Taibah Univ Med Sci. 19(2):338-350. doi: 10.1016/j.jtumed.2024.01.001.
[91]. Praveen, M. (2024). Multi-epitope-based vaccine designing against Junín virus glycoprotein: immunoinformatics approach. Futur J Pharm Sci 10, 29. 12-24.
[92]. Praveen, M., Morales-Bayuelo, A. (2023). Drug Designing against VP4, VP7 and NSP4 of Rotavirus Proteins – Insilico studies, Mor. J. Chem., 14(6), 729-741.
[93]. Praveen, M., Ullah, I., Buendia, R., Khan, I. A., Sayed, M. G., Kabir, R., Bhat, M. A., Yaseen, M. (2024). Exploring Potentilla nepalensis Phytoconstituents: Integrated Strategies of Network Pharmacology, Molecular Docking, Dynamic Simulations, and MMGBSA Analysis for Cancer Therapeutic Targets Discovery. Pharmaceuticals (Basel). 17(1):134. doi: 10.3390/ph17010134.
[94]. Rajman, I. 2008. PK/PD modelling and simulations: utility in drug development. Drug Discovery Today, 13(7-8), 341-346.
[95]. Robak, T., Robak, E. 2012. Tyrosine kinase inhibitors as potential drugs for B-cell lymphoid malignancies and autoimmune disorders. Expert opinion on investigational drugs, 21(7), 921–947. https://doi.org/10.1517/13543784.2012.685650
[96]. Robles, O., Romo, D. 2014. Chemo- and site-selective derivatizations of natural products enabling biological studies. Natural Product Reports, 31(3), 318–334.
[97]. Rohatagi, S., Carrothers, T. J., Kuwabara‐Wagg, J., Khariton, T. 2009. Is a thorough QTc study necessary? The role of modeling and simulation in evaluating the QTc prolongation potential of drugs. The Journal of Clinical Pharmacology, 49(11), 1284-1296.
[98]. Sahu, P., Pinkalwar, N., Dubey, R. D., et al. 2011. Biomarkers: An emerging tool for diagnosis of a disease and drug development. Asian Journal of Research in Pharmaceutical Science, 1(1), 9-16.
[99]. Sarawek, S., Li, L., Yu, X., et al. 2009. Examination of the Utility of the High Throughput In Vitro Metabolic Stability Assay to Estimate In Vivo Clearance in the Mouse. The Open Drug Metabolism Journal, 3, 31–42. https://doi.org/10.2174/1874073100903010031
[100]. Sawada, Y., Kawai, R., McManaway, M., et al. 1991. Kinetic analysis of transport and opioid receptor binding of 3H-cyclofoxy in rat brain in vivo: implications for human studies. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 11(2), 183–203. https://doi.org/10.1038/jcbfm.1991.51.
[101]. Sheiner, L., Steimer, J.-L. 2000. Pharmacokinetic/pharmacodynamic modeling in drug development. Annual Review of Pharmacology and Toxicology, 40(1), 67-95.
[102]. Simeoni, M., Magni, P., Cammia, C., et al. 2004. Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer research, 64(3), 1094–1101. https://doi.org/10.1158/0008-5472.can-03-2524.
[103]. Slifstein, M., Abi-Dargham, A. 2017. Recent developments in molecular brain imaging of neuropsychiatric disorders. In Seminars in Nuclear Medicine, 47(1), 54-63.
[104]. Soars, M. G., Webborn, P. J., Riley, R. J. 2009. Impact of hepatic uptake transporters on pharmacokinetics and drug− drug interactions: use of assays and models for decision making in the pharmaceutical industry. Molecular Pharmaceutics, 6(6), 1662-1677.
[105]. Stevens, J., Ploeger, B. A., Hammarlund-Udenaes, M., et al. 2012. Mechanism-based PK-PD model for the prolactin biological system response following an acute dopamine inhibition challenge: quantitative extrapolation to humans. Journal of pharmacokinetics and pharmacodynamics, 39(5), 463–477. https://doi.org/10.1007/s10928-012-9262-4
[106]. Tabrizi, M. A., Tseng, C.-M. L., Roskos, L. K. 2006. Elimination mechanisms of therapeutic monoclonal antibodies. Drug Discovery Today, 11(1-2), 81-88.
[107]. Tardiff, D. F., Lindquist, S. 2013. Phenotypic screens for compounds that target the cellular pathologies underlying Parkinson’s disease. Drug Discovery Today: Technologies, 10(1), e121-e128.
[108]. Terstappen, G. C., Schlüpen, C., Raggiaschi, R., Gaviraghi, G. 2007. Target deconvolution strategies in drug discovery. Nature Reviews Drug Discovery, 6(11), 891-903.
[109]. Tewari, T., Mukherjee, S. 2010. Microdosing: concept, application and relevance. Perspectives in Clinical Research, 1(2), 61.
[110]. Thanga Mariappan, T., Mandlekar, S., Marathe, P. 2013. Insight into Tissue Unbound Concentration: Utility in Drug Discovery and Development. Current Drug Metabolism, 14(3), 324-340.
[111]. Tuntland, T., Ethell, B., Kosaka, T., et al. 2014. Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research. Frontiers in pharmacology, 5, 174. https://doi.org/10.3389/fphar.2014.00174
[112]. Uchida, Y., Tachikawa, M., Obuchi, W., et al. 2013. A study protocol for quantitative targeted absolute proteomics (QTAP) by LC-MS/MS: application for inter-strain differences in protein expression levels of transporters, receptors, claudin-5, and marker proteins at the blood-brain barrier in ddY, FVB, and C57BL/6J mice. Fluids and barriers of the CNS, 10(1), 21. https://doi.org/10.1186/2045-8118-10-21
[113]. Uchida, Y., Zhang, Z., Tachikawa, M., Terasaki, T. 2015. Quantitative targeted absolute proteomics of rat blood–cerebrospinal fluid barrier transporters: Comparison with a human specimen. Journal of Neurochemistry, 134(6), 1104-1115.
[114]. Uchizono, J. A., Lane, J. R. 2007. Empirical pharmacokinetic/pharmacodynamic models. In Pharmacometrics: The Science of Quantitative Pharmacology (pp. 529-545).
[115]. van den Anker, J., Reed, M. D., Allegaert, K., Kearns, G. L. 2018. Developmental changes in pharmacokinetics and pharmacodynamics. The Journal of Clinical Pharmacology, 58, S10-S25.
[116]. van den Brink, W. J., Elassaiss-Schaap, J., Gonzalez-Amoros, B., et al. 2017. Multivariate pharmacokinetic/pharmacodynamic (PKPD) analysis with metabolomics shows multiple effects of remoxipride in rats. European journal of pharmaceutical sciences: official journal of the European Federation for Pharmaceutical Sciences, 109, 431–440. https://doi.org/10.1016/j.ejps.2017.08.031
[117]. van den Brink, W. J., Wong, Y. C., Gülave, B., et al. 2017. Revealing the neuroendocrine response after remoxipride treatment using multi-biomarker discovery and quantifying it by PK/PD modeling. The AAPS Journal, 19, 274-285.
[118]. van der Greef, J., McBurney, R. N. 2005. Rescuing drug discovery: in vivo systems pathology and systems pharmacology. Nature Reviews Drug Discovery, 4(12), 961-967.
[119]. Van Rossum, J., Van Koppen, A. T. J. 1968. Kinetics of psycho-motor stimulant drug action. European Journal of Pharmacology, 2(5), 405-408.
[120]. Walkup, G. K., You, Z., Ross, P. L., et al. 2015. Translating slow-binding inhibition kinetics into cellular and in vivo effects. Nature chemical biology, 11(6), 416–423. https://doi.org/10.1038/nchembio.1796
[121]. Wang, S., Guo, P., Wang, X., et al.2008. Preclinical pharmacokinetic/pharmacodynamic models of gefitinib and the design of equivalent dosing regimens in EGFR wild-type and mutant tumor models. Molecular Cancer Therapeutics, 7(2), 407-417.
[122]. Wang, X., Barbosa, J., Blomgren, P., et al. 2017. Discovery of Potent and Selective Tricyclic Inhibitors of Bruton’s Tyrosine Kinase with Improved Druglike Properties. ACS medicinal chemistry letters, 8(6), 608–613. https://doi.org/10.1021/acsmedchemlett.7b00103
[123]. Wang, Y., Welty, D. R. 1996. The simultaneous estimation of the influx and efflux blood-brain barrier permeabilities of gabapentin using a microdialysis-pharmacokinetic approach. Pharmaceutical Research, 13, 398-403.
[124]. Westerhout, J., Ploeger, B., Smeets, J., et al. 2012. Physiologically based pharmacokinetic modeling to investigate regional brain distribution kinetics in rats. The AAPS journal, 14(3), 543–553. https://doi.org/10.1208/s12248-012-9366-1
[125]. Westerhout, J., Van den Berg, D.-J., Hartman, R., et al. 2014. Prediction of methotrexate CNS distribution in different species–Influence of disease conditions. European Journal of Pharmaceutical Sciences, 57, 11-24.
[126]. Whiteside, G. T., Kennedy, J. D. 2011. Consideration of pharmacokinetic pharmacodynamic relationships in the discovery of new pain drugs.
[127]. Wong, Y. C., Ilkova, T., van Wijk, R. C., et al. 2018. Development of a population pharmacokinetic model to predict brain distribution and dopamine D2 receptor occupancy of raclopride in non-anesthetized rat. European Journal of Pharmaceutical Sciences, 111, 514-525.
[128]. Yamamoto, Y., Välitalo, P. A., van den Berg, D. J., et al. 2017. A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations. Pharmaceutical research, 34(2), 333–351. https://doi.org/10.1007/s11095-016-2065-3.
[129]. Yamasaki, Y., Tiran, J., Albisser, A. M. 1984. Modeling glucose disposal in diabetic dogs fed mixed meals. American Journal of Physiology-Endocrinology and Metabolism, 246(1), E52-E61.
[130]. Yao, Z., DuBois, D. C., Almon, R. R., Jusko, W. J. 2008. Pharmacokinetic/pharmacodynamic modeling of corticosterone suppression and lymphocytopenia by methylprednisolone in rats. Journal of Pharmaceutical Sciences, 97(7), 2820-2832.
[131]. Yu, R. Z., Lemonidis, K. M., Graham, M. J., et al. 2009. Cross-species comparison of in vivo PK/PD relationships for second-generation antisense oligonucleotides targeting apolipoprotein B-100. Biochemical pharmacology, 77(5), 910–919. https://doi.org/10.1016/j.bcp.2008.11.005.
[132]. Yu, X. Q., Kramer, J., Moran, L., et al. 2010. Pharmacokinetic/pharmacodynamic modelling of 2-acetyl-4(5)-tetrahydroxybutyl imidazole-induced peripheral lymphocyte sequestration through increasing lymphoid sphingosine 1-phosphate. Xenobiotica; the fate of foreign compounds in biological systems, 40(5), 350–356. https://doi.org/10.3109/00498251003611376
[133]. Yu, X.-Q., Wilson, A. G. 2010. The role of pharmacokinetic and pharmacokinetic/pharmacodynamic modeling in drug discovery and development. Future Medicinal Chemistry, 2(6), 923-928.
[134]. Zhang, H.-Y. 2005. One-compound-multiple-targets strategy to combat Alzheimer’s disease. FEBS Letters, 579(24), 5260–5264.
[135]. Zuegge, J., Schneider, G., Coassolo, P., Lavé, T. 2001. Prediction of hepatic metabolic clearance: comparison and assessment of prediction models. Clinical Pharmacokinetics, 40, 553-563.