| 1. |
- Ayoun Alsoud, Rami, et al.
(author)
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Combined quantitative tuberculosis biomarker model for time-to-positivity and colony forming unit to support tuberculosis drug development
- 2023
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In: Frontiers in Pharmacology. - : Frontiers Media S.A.. - 1663-9812. ; 14
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Journal article (peer-reviewed)abstract
- Biomarkers are quantifiable characteristics of biological processes. In Mycobacterium tuberculosis, common biomarkers used in clinical drug development are colony forming unit (CFU) and time-to-positivity (TTP) from sputum samples. This analysis aimed to develop a combined quantitative tuberculosis biomarker model for CFU and TTP biomarkers for assessing drug efficacy in early bactericidal activity studies. Daily CFU and TTP observations in 83 previously patients with uncomplicated pulmonary tuberculosis after 7 days of different rifampicin monotherapy treatments (10-40 mg/kg) from the HIGHRIF1 study were included in this analysis. The combined quantitative tuberculosis biomarker model employed the Multistate Tuberculosis Pharmacometric model linked to a rifampicin pharmacokinetic model in order to determine drug exposure-response relationships on three bacterial sub-states using both the CFU and TTP data simultaneously. CFU was predicted from the MTP model and TTP was predicted through a time-to-event approach from the TTP model, which was linked to the MTP model through the transfer of all bacterial sub-states in the MTP model to a one bacterial TTP model. The non-linear CFU-TTP relationship over time was well predicted by the final model. The combined quantitative tuberculosis biomarker model provides an efficient approach for assessing drug efficacy informed by both CFU and TTP data in early bactericidal activity studies and to describe the relationship between CFU and TTP over time.
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| 2. |
- Ayoun Alsoud, Rami, et al.
(author)
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Model-based effect evaluation of a novel Mmpl3 inhibitor in C3HeB/FeJ compared to BALB/c mouse models and translation to humans
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Other publication (other academic/artistic)abstract
- Background and Purpose: During tuberculosis drug development, the design of early clinical studies is informed by preclinical animal models. The aim of this work was to describe the exposure-response relationship of a novel inhibitor of mycobacterial MmpL3, prodrug MPL-447, in C3HeB/FeJ mice with non-necrotic or necrotic lesions, and to compare to chronic BALB/c mice information.Experimental Approach: C3HeB/FeJ mice were randomised to placebo and three treatment groups (25, 50 or 100 mg/kg MPL-447). Colony forming unit (CFU) were obtained until week 8 post-treatment. Semi-mechanistic modelling was used to describe growth and killing in relation to exposure. Early bactericidal activity after 14 days (EBA0-14) in humans was predicted using the final model, translational factors and allometric scaling of pharmacokinetics to humans and compared to chronic BALB/c.Key Results: The final model showed 1100% growth and 42% killing of the fast-multiplying bacteria in C3HeB/FeJ mice with necrotic lesions compared to those with non-necrotic lesions. Simulations revealed similar log10CFU reduction on day 14 in C3HeB/FeJ mice with non-necrotic lesions as in chronic BALB/c mice in response to treatment, but 1.7-fold lower reduction in C3HeB/FeJ mice with necrotic lesions. Similar human EBA0-14 was predicted irrespective of the mouse model used. Conclusion and Implications: The difference in killing of fast-multiplying bacteria in C3HeB/FeJ mice with necrotic lesions compared to C3HeB/FeJ mice without or chronic BALB/C mice was not translated to human early clinical predictions, most likely due to low abundance of these bacteria in humans.
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| 3. |
- Ayoun Alsoud, Rami, et al.
(author)
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Model-based interspecies scaling for predicting human pharmacokinetics of CB 4332, a complement factor I protein
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Other publication (other academic/artistic)abstract
- The extrapolation of a protein pharmacokinetics (PK) from preclinical to clinical studies can be less reliable than for small molecules. CB 4332 is a 150 kDa recombinant complement factor I (CFI) protein. In order to support clinical development, interspecies scaling of CB 4332 using traditional and model-based approaches was performed to inform first-in-human (FIH) dose selection. Plasma concentration versus time data from four preclinical PK studies of single intravenous (i.v.) and subcutaneous (s.c.) CB 4332 dosing in mice, rats and nonhuman primates (NHPs) were modeled simultaneously using naive pooling including allometric scaling. The human-equivalent dose was calculated using the preclinical no observed adverse effect level (NOAEL) as part of the dose-by-factor approach. Pharmacokinetic modelling of CB 4332 revealed species-specific differences in the elimination, which was accounted for by including an additional rat-specific clearance. Signs of anti-drug antibodies (ADA) formation in all rats and some NHPs were observed. Consequently, an additional ADA-induced clearance parameter was estimated including the time of onset. Using the traditional dose-by-factor approach, a maximum recommended starting s.c. dose of 0.9 mg/kg once weekly was calculated using the NOAEL observed in NHPs. The model-based clinical trial simulations predicted it to result in a trough concentration at steady state 12.8% of the determined efficacy target for CB 4332 in humans. Interspecies scaling was performed for CB 4332 using traditional and model-based scaling, where PK modeling allowed the inclusion of preclinical PK information from three species, accounted for potential effects of ADA and species differences in elimination, and allowed the prediction of human PK for FIH dose selection.
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| 4. |
- Ayoun Alsoud, Rami, 1992-
(author)
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Pharmacometric tools to support translational drug development
- 2024
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Doctoral thesis (other academic/artistic)abstract
- The use of model-informed drug development has been shown to save significant costs and improve decision making early in the drug development process. The work in this PhD thesis aimed to employ pharmacometric tools to support translational drug development from the preclinical to the late clinical stages.Pharmacometric modeling was used to characterize the treatment-shortening potential of different anti tuberculosis regimens. The results provided additional evidence in favor of the treatment-shortening capacity of the BPaMZ regimen over BPaL and standard of care, HRZE.Pharmacokinetic-pharmacodynamic (PKPD) modeling was used to enable the evaluation of the exposure-response of a new anti-tubercular drug, MPL-447, in C3HeB/FeJ mice, thought to be of a translational value in tuberculosis drug development. Model-based evaluation revealed a significant impact of necrotic lesion development in mice on both bacterial growth and sensitivity to treatment with MPL-447, highlighting the significance of accounting for the heterogenous lesion profile in the C3HeB/FeJ mouse model when evaluating drug efficacy.Pharmacokinetic (PK) modeling was employed to perform interspecies PK scaling of the CB 4332 protein using information from three preclinical species. This approach accounted for the impact of immunogenicity and species-related differences in elimination. Simulations predicted the protein plasma concentrations in humans after different dosing regimens and suggested that a 7 mg/kg dose would be required to reach the target at steady-state.Using combined biomarker data, PKPD modeling was employed to simultaneously analyze two tuberculosis efficacy biomarkers. The final biomarker model facilitated the prediction of the relationship between the two biomarkers over time. With this modeling framework, missing biomarker data can be predicted using information from the other biomarker.Several model-based approaches were also explored to evaluate pediatric study power in rare diseases. These approaches were performed analyzing pediatric data alone or combined with the adult data. While Bayesian priors performed well when analyzing pediatric data alone, less technical modeling approaches proved sufficient when pediatric and adult data were combined.In conclusion, the research presented in this thesis has addressed various challenges encountered in translational drug development. The work has contributed to the evaluation of new anti-tubercular drugs and regimens, the assessment of newly proposed animal models, and optimizing the utilization of biomarker information. Furthermore, this thesis has provided insights into the selection of First-in-Human dose for a protein, showcasing the applicability of model-based approaches in this critical decision-making process. The research has contributed to improving analysis approaches for pediatrics in rare diseases.
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| 5. |
- Faraj, Alan, et al.
(author)
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Model-based approaches to prospectively power pediatric pharmacokinetic trials with limited sample size
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Other publication (other academic/artistic)abstract
- Rare disease studies in pediatric subjects are challenging due to small sample sizes. Pharmacokinetic (PK) information in pediatric subjects is important and often used for matching strategy towards adults informing pediatric development program. Prior to studying PK in children, it is important to optimize the sparse sampling schedule and show that the study is designed to estimate key PK parameters with sufficient certainty. In this work, the sampling schedule in children was optimized for marzeptacog alfa activated (MarzAA) and dalcinonacog alfa (DalcA), two drugs in development for treatment of hemophilia. Subsequently, evaluation of different model-based approaches to calculate the power to estimate clearance (CL) and volume of distribution (V) using a fixed sample size (n=24) was performed. Usage of Bayesian priors (up to 2x inflation of the adult priors) performed well (power 80 %), but with lower power with decreasing informativeness (5x and 10x inflation of the adult priors), in particular for DalcA. Reusing the full adult model or a simplified model for standalone analysis of the pediatric data did not perform well (<80% power). Fixing the adult PK parameters except for CL and V performed well when pooling adult and pediatric data (power 100 %). In general, the power to estimate V alone or CL together with V was lower than for CL, indicating that the sampling schedules were more informative for CL. Although Bayesian prior approaches were shown to perform well without need of pooling data, other approaches that require less technical expertise and no need for simplification of the adult model were found to be good alternatives when pooling of data is possible.
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| 6. |
- Mudde, Saskia E., et al.
(author)
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Predictive Modeling to Study the Treatment-Shortening Potential of Novel Tuberculosis Drug Regimens, Toward Bundling of Preclinical Data
- 2022
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In: Journal of Infectious Diseases. - : Oxford University Press. - 0022-1899 .- 1537-6613. ; 225:11, s. 1876-1885
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Journal article (peer-reviewed)abstract
- Background Given the persistently high global burden of tuberculosis, effective and shorter treatment options are needed. We explored the relationship between relapse and treatment length as well as interregimen differences for 2 novel antituberculosis drug regimens using a mouse model of tuberculosis infection and mathematical modeling. Methods Mycobacterium tuberculosis-infected mice were treated for up to 13 weeks with bedaquiline and pretomanid combined with moxifloxacin and pyrazinamide (BPaMZ) or linezolid (BPaL). Cure rates were evaluated 12 weeks after treatment completion. The standard regimen of isoniazid, rifampicin, pyrazinamide, and ethambutol (HRZE) was evaluated as a comparator. Results Six weeks of BPaMZ was sufficient to achieve cure in all mice. In contrast, 13 weeks of BPaL and 24 weeks of HRZE did not achieve 100% cure rates. Based on mathematical model predictions, 95% probability of cure was predicted to occur at 1.6, 4.3, and 7.9 months for BPaMZ, BPaL, and HRZE, respectively. Conclusion This study provides additional evidence for the treatment-shortening capacity of BPaMZ over BPaL and HRZE. To optimally use preclinical data for predicting clinical outcomes, and to overcome the limitations that hamper such extrapolation, we advocate bundling of available published preclinical data into mathematical models. By combining the evaluation of treatment efficacy of anti-tuberculosis drug regimens in a mouse tuberculosis infection model with mathematical modeling, it was found that BPaMZ had a higher treatment-shortening potential than BPaL, compared to the standard HRZE regimen.
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| 7. |
- Van Wijk, Rob C, 1991-, et al.
(author)
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Model-informed drug discovery and development strategy for the rapid development of anti-tuberculosis drug combinations
- 2020
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In: Applied Sciences. - : MDPI AG. - 1454-5101 .- 2076-3417. ; 10
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Journal article (peer-reviewed)abstract
- 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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