Literature DB >> 23962236

At the bench: the key role of PK-PD modelling in enabling the early discovery of biologic therapies.

Mark Penney1, Balaji Agoram.   

Abstract

Pharmacokinetic-pharmacodynamic (PK-PD) modelling is already used extensively in pre-clinical and clinical drug development to characterize drug candidates quantitatively, aid go/no-go decisions and to inform future trial design and optimal dosing regimens. Less well known, although arguably as powerful, is its application at the earliest stages of drug development, at target selection and lead selection, where these same techniques can be used to predict and so bring forward drug candidates with the necessary characteristics or, for unachievable requirements, allow the abandonment of the programme for the minimum spend of time and cost. We consider three examples that illustrate the power of the application of modelling at this early stage. We start with the simple case of determining the optimal characteristics for a monoclonal antibody against a soluble ligand with its application to the investment decision for the development of best-in-class compounds. This is extended to the more complex situation of the target protein having an endogenous, inhibitory binding protein. We then illustrate how using physiologically-based pharmacokinetic modelling enables the appropriate engineering and testing of biological therapeutics for optimal PK-PD characteristics. These examples illustrate how a minimal investment in modelling achieves orders of magnitude better returns in choosing the correct targets, mechanism of action and candidate characteristics to progress to clinical trials, streamlining drug development and delivering better medicines to patients.
© 2013 The British Pharmacological Society.

Entities:  

Keywords:  PBPK modelling; PK-PD modelling; antibody engineering

Mesh:

Substances:

Year:  2014        PMID: 23962236      PMCID: PMC4004394          DOI: 10.1111/bcp.12225

Source DB:  PubMed          Journal:  Br J Clin Pharmacol        ISSN: 0306-5251            Impact factor:   4.335


  18 in total

Review 1.  Predicting the impact of physiological and biochemical processes on oral drug bioavailability.

Authors:  B Agoram; W S Woltosz; M B Bolger
Journal:  Adv Drug Deliv Rev       Date:  2001-10-01       Impact factor: 15.470

2.  (Q)SAR modeling and safety assessment in regulatory review.

Authors:  N L Kruhlak; R D Benz; H Zhou; T J Colatsky
Journal:  Clin Pharmacol Ther       Date:  2012-01-18       Impact factor: 6.875

3.  Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug development.

Authors:  C C Peck; W H Barr; L Z Benet; J Collins; R E Desjardins; D E Furst; J G Harter; G Levy; T Ludden; J H Rodman
Journal:  Clin Pharmacol Ther       Date:  1992-04       Impact factor: 6.875

4.  Integrating cell-level kinetic modeling into the design of engineered protein therapeutics.

Authors:  Balaji M Rao; Douglas A Lauffenburger; K Dane Wittrup
Journal:  Nat Biotechnol       Date:  2005-02       Impact factor: 54.908

5.  Properties of human IgG1s engineered for enhanced binding to the neonatal Fc receptor (FcRn).

Authors:  William F Dall'Acqua; Peter A Kiener; Herren Wu
Journal:  J Biol Chem       Date:  2006-06-21       Impact factor: 5.157

Review 6.  Model-based drug development.

Authors:  R L Lalonde; K G Kowalski; M M Hutmacher; W Ewy; D J Nichols; P A Milligan; B W Corrigan; P A Lockwood; S A Marshall; L J Benincosa; T G Tensfeldt; K Parivar; M Amantea; P Glue; H Koide; R Miller
Journal:  Clin Pharmacol Ther       Date:  2007-05-23       Impact factor: 6.875

7.  Concepts and challenges in quantitative pharmacology and model-based drug development.

Authors:  Liping Zhang; Marc Pfister; Bernd Meibohm
Journal:  AAPS J       Date:  2008-11-12       Impact factor: 4.009

Review 8.  On the prediction of the human response: a recycled mechanistic pharmacokinetic/pharmacodynamic approach.

Authors:  Guy M L Meno-Tetang; Philip J Lowe
Journal:  Basic Clin Pharmacol Toxicol       Date:  2005-03       Impact factor: 4.080

9.  Interleukin-18 binding protein: a novel modulator of the Th1 cytokine response.

Authors:  D Novick; S H Kim; G Fantuzzi; L L Reznikov; C A Dinarello; M Rubinstein
Journal:  Immunity       Date:  1999-01       Impact factor: 31.745

Review 10.  Immunological and inflammatory functions of the interleukin-1 family.

Authors:  Charles A Dinarello
Journal:  Annu Rev Immunol       Date:  2009       Impact factor: 28.527

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  4 in total

1.  Optimal Affinity of a Monoclonal Antibody: Guiding Principles Using Mechanistic Modeling.

Authors:  Abhinav Tiwari; Anson K Abraham; John M Harrold; Anup Zutshi; Pratap Singh
Journal:  AAPS J       Date:  2016-12-21       Impact factor: 4.009

Review 2.  Which factors matter the most? Revisiting and dissecting antibody therapeutic doses.

Authors:  Yu Tang; Xiaobing Li; Yanguang Cao
Journal:  Drug Discov Today       Date:  2021-04-22       Impact factor: 8.369

3.  Pharmacokinetic-Pharmacodynamic Analysis on Inflammation Rat Model after Oral Administration of Huang Lian Jie Du Decoction.

Authors:  Wei Ren; Ran Zuo; Yao-Nan Wang; Hong-Jie Wang; Jian Yang; Shao-Kun Xin; Ling-Yu Han; Hai-Yu Zhao; Shu-Yan Han; Bo Gao; Hao Hu; Yuan-Jia Hu; Bao-Lin Bian; Nan Si
Journal:  PLoS One       Date:  2016-06-09       Impact factor: 3.240

Review 4.  Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions.

Authors:  Yu Tang; Yanguang Cao
Journal:  Pharmaceutics       Date:  2021-03-21       Impact factor: 6.321

  4 in total

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