Comparisons of Analysis Methods for Assessment of Pharmacodynamic Interactions Including Design Recommendations

Chen, Chunli; Wicha, Sebastian G.; Nordgren, Rikard; Simonsson, Ulrika S. H.

doi:10.1208/s12248-018-0239-0

Cited by 9 publications

(21 citation statements)

References 12 publications

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“…the concentration stimulating 20% of the of the maximum effect), 𝐼𝐶 (i.e. the concentration simulating 80% of the maximum effect) and a 'high' concentration covering the clinically relevant maximum concentrations, adapted from Chen et al (16).…”

Section: Determination Of Single Drug Effects From the Growth Inhibit...mentioning

confidence: 99%

A new in vitro checkerboard-parasite reduction ratio interaction assay for early de-risk of clinical development of antimalarial combinations

Wicha

Walz

Cherkaoui-Rbati

et al. 2022

Preprint

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The development and spread of drug resistant phenotypes substantially threaten malaria control efforts. Combination therapies have the potential to minimize the risk of resistance development but require intensive preclinical studies to determine optimal combination and dosing regimens. To support the selection of new combinations, we developed a novel in vitro-in silico combination approach to help identify the pharmacodynamic interactions of the two antimalarial drugs which can be plugged into a pharmacokinetic/pharmacodynamic model built with human monotherapies parasitological data to predict the parasitological endpoints of the combination. This allows to optimally select drug combinations and doses for the clinical development of antimalarials. With this assay, we successfully predicted the endpoints of two phase 2 clinical trials in patients with the artefenomel - piperaquine and artefenomel - ferroquine drug combinations. Besides, the predictive performance of our novel in vitro model was equivalent to the humanized mouse model outcome. Lastly, our more granular in vitro combination assay provided additional insights into the pharmacodynamic drug interactions compared to the in vivo systems, e.g. a concentration-dependent change in the Emax and the EC50 values of piperaquine or artefenomel or a directional reduction of the EC50 of ferroquine by artefenomel and a directional reduction of Emax of ferroquine by artefenomel. Overall, this novel in vitro-in silico-based technology will significantly improve and streamline the economic development of new drug combinations in malaria and potentially also in other therapeutic areas.

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Section: Determination Of Single Drug Effects From the Growth Inhibit...mentioning

confidence: 99%

A new in vitro checkerboard-parasite reduction ratio interaction assay for early de-risk of clinical development of antimalarial combinations

Wicha

Walz

Cherkaoui-Rbati

et al. 2022

Preprint

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“…The MTP-GPDI model has been further employed to successfully evaluate and quantify the PD interactions of anti-TB drug combinations in mice [145]. Furthermore, it has been demonstrated that the GPDI model outperforms conventional methods in the evaluation of PD interactions for TB drugs [146].…”

Section: Prediction Of Human Drug-drug Interactionsmentioning

confidence: 99%

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

et al. 2020

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The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

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“…Optimal experimental designs for in vitro experiments for identification of PD interactions have been studied, and a rational design using only information about the EC 50 of each drug from monotherapy is used to inform the design of the PD interaction studies. 8 It is important that EBA studies are designed and analyzed in such a manner that an efficient clinical trial study is designed and analyzed with high power. Earlier work has shown that a PKPD model approach using the same underlying disease model as in this work, i.e., the MTP model, had twofold higher power for EBA studies compared with traditional studies.…”

Section: Articlementioning

confidence: 99%

“…The MTP-GPDI approach has been used to identify PD interactions both in vitro 6 and in vivo 7 and has been shown to be superior compared with other approaches for identifying PD interactions. 8 A forward translation framework for clinical exposure-response forecasting in EBA studies based on preclinical in vitro information using the MTP model has been successfully developed for rifampicin. 3 In this work, we developed a translational approach which allows for forward translation to extend the framework to predict the efficacy of the drug combination in EBA studies.…”

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confidence: 99%

Translational Model‐Informed Approach for Selection of Tuberculosis Drug Combination Regimens in Early Clinical Development

Susanto

Wicha

et al. 2020

Clin Pharma and Therapeutics

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The development of optimal treatment regimens in tuberculosis (TB) remains challenging due to the need of combination therapy and possibility of pharmacodynamic (PD) interactions. Preclinical information about PD interactions needs to be used more optimally when designing early bactericidal activity (EBA) studies. In this work, we developed a translational approach which can allow for forward translation to predict efficacy of drug combination in EBA studies using the Multistate Tuberculosis Pharmacometric (MTP) and the General Pharmacodynamic Interaction (GPDI) models informed by in vitro static time-kill data. These models were linked with translational factors to account for differences between the in vitro system and humans. Our translational MTP-GPDI model approach was able to predict the EBA 0-2 days , EBA 0-5 days , and EBA 0-14 days from different EBA studies of rifampicin and isoniazid in monotherapy and combination. Our translational model approach can contribute to an optimal dose selection of drug combinations in early TB clinical trials.A more effective regimen with a shorter treatment duration is an urgent need to provide more efficient treatment options for patients with pulmonary tuberculosis (TB). Since TB treatment requires multidrug regimens, pharmacodynamics (PD) interactions can be a challenge for developing optimal regimens. The typical early bactericidal activity (EBA) study in a TB phase IIa trial acts as the first "proof-of-concept" study of microbiological activity in humans when the drug is given as monotherapy for 2 weeks. Few EBA studies have explored combinations of drugs, 1,2 but traditionally the EBA studies have played a role in dose selection for the phase IIb trials where the results from the EBA study informs the combination regimen to be evaluated. However, the most optimal dose in monotherapy may not be the optimal dose for combination treatment. Therefore, it is not possible to study the most optimal dose combinations in humans due to the many possible combinations. An alternative is to use preclinical in vitro information where it is possible to study a large set of combinations in order to define the PD interaction space. However, prediction of clinical efficacy based on in vitro information needs to account for translational factors such as human pharmacokinetics (PK), target site exposure, and mycobacterial factors, such as bacterial growth phase, postantibiotic effect (PAE), and minimum inhibitory concentration (MIC) distribution. 3

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Comparisons of Analysis Methods for Assessment of Pharmacodynamic Interactions Including Design Recommendations

Cited by 9 publications

References 12 publications

A new in vitro checkerboard-parasite reduction ratio interaction assay for early de-risk of clinical development of antimalarial combinations

A new in vitro checkerboard-parasite reduction ratio interaction assay for early de-risk of clinical development of antimalarial combinations

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

Translational Model‐Informed Approach for Selection of Tuberculosis Drug Combination Regimens in Early Clinical Development

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