Pub Date : 2024-12-17DOI: 10.1007/s10928-024-09956-1
Eshita Khera, Lekshmi Dharmarajan, Dominik Hainzl, Volker Engelhardt, Helena Vostiarova, John Davis, Nicolas Ebel, Kuno Wuersch, Vincent Romanet, Sherif Sharaby, Jeffrey D Kearns
Antibody drug conjugates (ADC) are a promising class of oncology therapeutics consisting of an antibody conjugated to a payload via a linker. DYP688 is a novel ADC comprising of a signaling protein inhibitor payload (FR900359) that undergoes unique on-antibody inactivation in plasma, resulting in complex pharmacology. To assess the impact of FR inactivation on DYP688 pharmacology and clinical developability, we performed translational modeling of preclinical PK and tumor growth inhibition (TGI) data, accompanied by mechanistic Krogh cylinder tumor modeling. Using a PK-TGI model, we identified a composite exposure-above-tumorostatic concentration (AUCTSC) metric as the PK-driver of efficacy. To underpin the mechanisms behind AUCTSC as the driver of efficacy, we performed quantitative systems pharmacology (QSP) modeling of DYP688 intratumoral pharmacokinetics and pharmacodynamics. Through exploratory simulations, we show that by deviating from canonical ADC design dogma, DYP688 has optimal FR900359 activity despite its transient inactivation. Finally, we performed the successful preclinical to clinical translation of DYP688 PK, including the payload inactivation kinetics, evidenced by good agreement of the predicted PK to the observed interim clinical PK. Overall, this work highlights early quantitative pharmacokinetics as a missing link in the ADC design-developability chasm.
{"title":"QSP modeling of a transiently inactivating antibody-drug conjugate highlights benefit of short antibody half life.","authors":"Eshita Khera, Lekshmi Dharmarajan, Dominik Hainzl, Volker Engelhardt, Helena Vostiarova, John Davis, Nicolas Ebel, Kuno Wuersch, Vincent Romanet, Sherif Sharaby, Jeffrey D Kearns","doi":"10.1007/s10928-024-09956-1","DOIUrl":"10.1007/s10928-024-09956-1","url":null,"abstract":"<p><p>Antibody drug conjugates (ADC) are a promising class of oncology therapeutics consisting of an antibody conjugated to a payload via a linker. DYP688 is a novel ADC comprising of a signaling protein inhibitor payload (FR900359) that undergoes unique on-antibody inactivation in plasma, resulting in complex pharmacology. To assess the impact of FR inactivation on DYP688 pharmacology and clinical developability, we performed translational modeling of preclinical PK and tumor growth inhibition (TGI) data, accompanied by mechanistic Krogh cylinder tumor modeling. Using a PK-TGI model, we identified a composite exposure-above-tumorostatic concentration (AUC<sub>TSC</sub>) metric as the PK-driver of efficacy. To underpin the mechanisms behind AUC<sub>TSC</sub> as the driver of efficacy, we performed quantitative systems pharmacology (QSP) modeling of DYP688 intratumoral pharmacokinetics and pharmacodynamics. Through exploratory simulations, we show that by deviating from canonical ADC design dogma, DYP688 has optimal FR900359 activity despite its transient inactivation. Finally, we performed the successful preclinical to clinical translation of DYP688 PK, including the payload inactivation kinetics, evidenced by good agreement of the predicted PK to the observed interim clinical PK. Overall, this work highlights early quantitative pharmacokinetics as a missing link in the ADC design-developability chasm.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 1","pages":"7"},"PeriodicalIF":2.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11652588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142846746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1007/s10928-024-09953-4
Guillermo Vigueras, Lucía Muñoz-Gil, Valerie Reinisch, Joana T Pinto
Current treatment recommendations mainly rely on rule-based protocols defined from evidence-based clinical guidelines, which are difficult to adapt for high-risk patients such as those with renal impairment. Consequently, unsuccessful therapies and the occurrence of adverse drug reactions are common. Within the context of personalized medicine, that tries to deliver the right treatment dose to maximize efficacy and minimize toxicity, the concept of model-informed precision dosing proposes the use of mechanistic models, like physiologically based pharmacokinetic (PBPK) modeling, to predict drug regimes outcomes. Nonetheless, PBPK models have limited capability when computing patients' centric optimized drug doses. Consequently, reinforcement learning (RL) has been previously used to personalize drug dosage. In this work we propose the first PBPK and RL-based precision dosing system for an orally taken drug (benazepril) considering a virtual population of patients with renal disease. Population based PBPK modeling is used in combination with RL for obtaining patient tailored dose regimes. We also perform patient stratification and feature selection to better handle dose tailoring problems. Based on patients' characteristics with best predictive capabilities, benazepril dose regimes are obtained for a population with features' diversity. Obtained regimes are evaluated based on PK parameters considered. Results show that the proof-of-concept approach herein is capable of learning good dosing regimes for most patients. The use of a PopPBPK model allowed to account for intervariability of patient characteristics and be more inclusive considering also non-frequent patients. Impact analysis of patients' features reveals that renal impairment is the main driver affecting RL capabilities.
{"title":"A PopPBPK-RL approach for precision dosing of benazepril in renal impaired patients.","authors":"Guillermo Vigueras, Lucía Muñoz-Gil, Valerie Reinisch, Joana T Pinto","doi":"10.1007/s10928-024-09953-4","DOIUrl":"10.1007/s10928-024-09953-4","url":null,"abstract":"<p><p>Current treatment recommendations mainly rely on rule-based protocols defined from evidence-based clinical guidelines, which are difficult to adapt for high-risk patients such as those with renal impairment. Consequently, unsuccessful therapies and the occurrence of adverse drug reactions are common. Within the context of personalized medicine, that tries to deliver the right treatment dose to maximize efficacy and minimize toxicity, the concept of model-informed precision dosing proposes the use of mechanistic models, like physiologically based pharmacokinetic (PBPK) modeling, to predict drug regimes outcomes. Nonetheless, PBPK models have limited capability when computing patients' centric optimized drug doses. Consequently, reinforcement learning (RL) has been previously used to personalize drug dosage. In this work we propose the first PBPK and RL-based precision dosing system for an orally taken drug (benazepril) considering a virtual population of patients with renal disease. Population based PBPK modeling is used in combination with RL for obtaining patient tailored dose regimes. We also perform patient stratification and feature selection to better handle dose tailoring problems. Based on patients' characteristics with best predictive capabilities, benazepril dose regimes are obtained for a population with features' diversity. Obtained regimes are evaluated based on PK parameters considered. Results show that the proof-of-concept approach herein is capable of learning good dosing regimes for most patients. The use of a PopPBPK model allowed to account for intervariability of patient characteristics and be more inclusive considering also non-frequent patients. Impact analysis of patients' features reveals that renal impairment is the main driver affecting RL capabilities.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 1","pages":"6"},"PeriodicalIF":2.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142813535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-10DOI: 10.1007/s10928-024-09950-7
Roberto Gomeni, F Bressolle-Gomeni
In randomized, placebo controlled clinical trials (RCT) in major depressive disorders (MDD), treatment response (TR) is estimated by the change from baseline at study-end (EOS) of the scores of clinical scales used for assessing disease severity. Treatment effect (TE) is estimated by the baseline-adjusted difference at EOS of TR between active treatments and placebo.The TE is function of treatment-specific and, non-specific (NSRT) effect (referred as placebo effect), and placebo response. The conventional statistical approaches used to estimate TE does not account for the potentially confounding effect of NSRT. This pragmatic approach is equivalent to assume that TE is independent of NSRT even if this assumption is not true, leading to potential risks of inflating false negative/positive results in presence of high proportion of subjects with high/low NSRT.The objective of this study was to develop a model informed framework to analyze the outcomes of RCTs using data driven models, non-linear-mixed effect approach, artificial intelligence, and propensity score weighted methodology (PSW) to control the confounding effect of treatment non-specific response on the estimated TE. The secondary objective was to explore the impact of relevant covariates (including the assessment of a dose-response relationship) on the outcomes of pooled data from two RCTs.The proposed PSW approach provides a critical tool for controlling the confounding effect of treatment non-specific response, to increase signal detection and to provide a reliable estimate of the 'true' treatment effect by controlling false negative results associated with excessively high treatment non-specific response.
{"title":"Model-informed approach to estimate treatment effect in placebo-controlled clinical trials using an artificial intelligence-based propensity weighting methodology to account for non-specific responses to treatment.","authors":"Roberto Gomeni, F Bressolle-Gomeni","doi":"10.1007/s10928-024-09950-7","DOIUrl":"10.1007/s10928-024-09950-7","url":null,"abstract":"<p><p>In randomized, placebo controlled clinical trials (RCT) in major depressive disorders (MDD), treatment response (TR) is estimated by the change from baseline at study-end (EOS) of the scores of clinical scales used for assessing disease severity. Treatment effect (TE) is estimated by the baseline-adjusted difference at EOS of TR between active treatments and placebo.The TE is function of treatment-specific and, non-specific (NSRT) effect (referred as placebo effect), and placebo response. The conventional statistical approaches used to estimate TE does not account for the potentially confounding effect of NSRT. This pragmatic approach is equivalent to assume that TE is independent of NSRT even if this assumption is not true, leading to potential risks of inflating false negative/positive results in presence of high proportion of subjects with high/low NSRT.The objective of this study was to develop a model informed framework to analyze the outcomes of RCTs using data driven models, non-linear-mixed effect approach, artificial intelligence, and propensity score weighted methodology (PSW) to control the confounding effect of treatment non-specific response on the estimated TE. The secondary objective was to explore the impact of relevant covariates (including the assessment of a dose-response relationship) on the outcomes of pooled data from two RCTs.The proposed PSW approach provides a critical tool for controlling the confounding effect of treatment non-specific response, to increase signal detection and to provide a reliable estimate of the 'true' treatment effect by controlling false negative results associated with excessively high treatment non-specific response.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 1","pages":"5"},"PeriodicalIF":2.2,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142801239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-10DOI: 10.1007/s10928-024-09949-0
Elham Haem, Mats O Karlsson, Sebastian Ueckert
Composite scale data consists of numerous categorical questions/items that are often summed as a total score and are commonly utilized as primary endpoints in clinical trials. These endpoints are conceptually discrete and constrained by nature. Item response theory (IRT) is a powerful approach for detecting drug effects in composite scale data from clinical trials, but estimating all parameters requires a large sample size and all item information, which may not be available. Therefore, total score models are often utilized. The most popular total score models are continuous variable (CV) models, but this strategy establishes assumptions that go against the integer nature, and typically also the bounded nature, of data. Bounded integer (BI) and Coarsened grid (CG) models respect the nature of the data. However, their power to detect drug effects has not been as thoroughly studied in clinical trials. When an IRT model is accessible, IRT-informed models (I-BI and I-CV) are promising methods in which the mean and variability of the total score at any position are extracted from the existing IRT model. In this study, total score data were simulated from the MDS-UPDRS motor subscale. Then, the power, type 1 error, and treatment effect bias of six total score models for detecting drug effects in clinical trials were explored. Further, it was investigated how the power, type 1 of error, and treatment effect bias for the I-BI and I-CV models were affected by mis-specified item information from the IRT model. The I-BI model demonstrated the highest statistical power, maintained an acceptable Type I error rate, and exhibited minimal bias, approaching zero. Following that, the I-CV, BI, and CG with Czado transformation (CG_Czado) models provided the maximum power. However, the CG_Czado model had inflated type 1 error under low sample size scenarios in each arm of clinical trials. The CG model among total score models displayed the lowest power and the most inflated type 1 error. Therefore, the results favor the I-BI model when an IRT model is available; otherwise, the BI model.
{"title":"Comparison of the power and type 1 error of total score models for drug effect detection in clinical trials.","authors":"Elham Haem, Mats O Karlsson, Sebastian Ueckert","doi":"10.1007/s10928-024-09949-0","DOIUrl":"10.1007/s10928-024-09949-0","url":null,"abstract":"<p><p>Composite scale data consists of numerous categorical questions/items that are often summed as a total score and are commonly utilized as primary endpoints in clinical trials. These endpoints are conceptually discrete and constrained by nature. Item response theory (IRT) is a powerful approach for detecting drug effects in composite scale data from clinical trials, but estimating all parameters requires a large sample size and all item information, which may not be available. Therefore, total score models are often utilized. The most popular total score models are continuous variable (CV) models, but this strategy establishes assumptions that go against the integer nature, and typically also the bounded nature, of data. Bounded integer (BI) and Coarsened grid (CG) models respect the nature of the data. However, their power to detect drug effects has not been as thoroughly studied in clinical trials. When an IRT model is accessible, IRT-informed models (I-BI and I-CV) are promising methods in which the mean and variability of the total score at any position are extracted from the existing IRT model. In this study, total score data were simulated from the MDS-UPDRS motor subscale. Then, the power, type 1 error, and treatment effect bias of six total score models for detecting drug effects in clinical trials were explored. Further, it was investigated how the power, type 1 of error, and treatment effect bias for the I-BI and I-CV models were affected by mis-specified item information from the IRT model. The I-BI model demonstrated the highest statistical power, maintained an acceptable Type I error rate, and exhibited minimal bias, approaching zero. Following that, the I-CV, BI, and CG with Czado transformation (CG_Czado) models provided the maximum power. However, the CG_Czado model had inflated type 1 error under low sample size scenarios in each arm of clinical trials. The CG model among total score models displayed the lowest power and the most inflated type 1 error. Therefore, the results favor the I-BI model when an IRT model is available; otherwise, the BI model.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 1","pages":"4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11632077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142801232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1007/s10928-024-09952-5
Sven Hoefman, Tamara van Steeg, Ingrid Ottevaere, Judith Baumeister, Stefaan Rossenu
Efgartigimod is a human IgG1 antibody Fc-fragment that lowers IgG levels through blockade of the neonatal Fc receptor (FcRn) and is being evaluated for the treatment of patients with severe autoimmune diseases mediated by pathogenic IgG autoantibodies. Engineered for increased FcRn affinity at both acidic and physiological pH, efgartigimod can outcompete endogenous IgG binding, preventing FcRn-mediated recycling of IgGs and resulting in increased lysosomal degradation. A population pharmacokinetic-pharmacodynamic (PKPD) model including FcRn binding was developed based on data from two healthy volunteer studies after single and repeated administration of efgartigimod. This model was able to simultaneously describe the serum efgartigimod and total IgG profiles across dose groups, using drug-induced FcRn receptor occupancy as driver of total IgG suppression. The model was expanded to describe the PKPD of efgartigimod in cynomolgus monkeys, rabbits, rats and mice. Most species differences were explainable by including the species-specific in vitro affinity for FcRn binding at pH 7.4 and by allometric scaling of the physiological parameters. In vitro-in vivo scaling proved crucial for translation success: the drug effect was over/underpredicted in rabbits/mice when ignoring the lower/higher binding affinity of efgartigimod for these species versus human, respectively. Given the successful model prediction of the PK and total IgG dynamics across species, it was concluded that the PKPD of efgartigimod can be characterized by target binding. From the model, it is suggested that the initial fast decrease of measurable unbound efgartigimod following dosing is the result of combined clearance of free drug and high affinity target binding, while the relatively slow terminal PK phase reflects release of bound drug from the receptor. High affinity target binding protects the drug from elimination and results in a sustained PD effect characterized by an increase in the IgG degradation rate constant with increasing target receptor occupancy.
{"title":"Translational population target binding model for the anti-FcRn fragment antibody efgartigimod.","authors":"Sven Hoefman, Tamara van Steeg, Ingrid Ottevaere, Judith Baumeister, Stefaan Rossenu","doi":"10.1007/s10928-024-09952-5","DOIUrl":"10.1007/s10928-024-09952-5","url":null,"abstract":"<p><p>Efgartigimod is a human IgG1 antibody Fc-fragment that lowers IgG levels through blockade of the neonatal Fc receptor (FcRn) and is being evaluated for the treatment of patients with severe autoimmune diseases mediated by pathogenic IgG autoantibodies. Engineered for increased FcRn affinity at both acidic and physiological pH, efgartigimod can outcompete endogenous IgG binding, preventing FcRn-mediated recycling of IgGs and resulting in increased lysosomal degradation. A population pharmacokinetic-pharmacodynamic (PKPD) model including FcRn binding was developed based on data from two healthy volunteer studies after single and repeated administration of efgartigimod. This model was able to simultaneously describe the serum efgartigimod and total IgG profiles across dose groups, using drug-induced FcRn receptor occupancy as driver of total IgG suppression. The model was expanded to describe the PKPD of efgartigimod in cynomolgus monkeys, rabbits, rats and mice. Most species differences were explainable by including the species-specific in vitro affinity for FcRn binding at pH 7.4 and by allometric scaling of the physiological parameters. In vitro-in vivo scaling proved crucial for translation success: the drug effect was over/underpredicted in rabbits/mice when ignoring the lower/higher binding affinity of efgartigimod for these species versus human, respectively. Given the successful model prediction of the PK and total IgG dynamics across species, it was concluded that the PKPD of efgartigimod can be characterized by target binding. From the model, it is suggested that the initial fast decrease of measurable unbound efgartigimod following dosing is the result of combined clearance of free drug and high affinity target binding, while the relatively slow terminal PK phase reflects release of bound drug from the receptor. High affinity target binding protects the drug from elimination and results in a sustained PD effect characterized by an increase in the IgG degradation rate constant with increasing target receptor occupancy.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 1","pages":"2"},"PeriodicalIF":2.2,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1007/s10928-024-09947-2
Sree Kurup, Nieves Velez de Mendizabal, Stephan Becker, Erica Bolella, Dorothy De Sousa, Gerd Fätkenheuer, Henning Gruell, Florian Klein, Jakob J Malin, Ulrike Schmid, Julia Korell
DZIF-10c (BI 767551) is a recombinant human monoclonal antibody of the IgG1 kappa isotype. It acts as a SARS-CoV-2 neutralizing antibody. DZIF-10c has been developed for both systemic exposure by intravenous infusion as well as for specific exposure to the respiratory tract by application as an inhaled aerosol generated by a nebulizer. An integrated preclinical/clinical semi-mechanistic population pharmacokinetic model was developed to characterize the exposure profile of DZIF-10c in the systemic circulation and lungs. To inform and reduce uncertainty around exposure in the lungs following different methods of dosing, preclinical cynomolgus monkey data was combined with human data using allometric scaling principles. Human serum concentrations of DZIF-10c from two clinical trials were combined with serum/plasma and lung epithelial lining fluid (ELF) concentrations from three preclinical studies to characterize the relationship between dosing, serum/plasma, and lung exposure. The final model was used to predict exposure in the lungs following different routes of administration. Simulations showed that inhalation provides immediate and relevant exposure in the lung ELF at a much lower dose compared with an infusion. Combining inhalation with intravenous therapy results in high and sustained DZIF-10c exposure in the lungs and systemic circulation, thereby combining the benefits of both routes of administration. By combining preclinical data with clinical data (via allometric scaling principles), the developed population pharmacokinetic model reduced uncertainty around exposure in the lungs allowing evaluation of alternative dosing strategies to achieve the desired concentrations of DZIF-10c in human lungs.
{"title":"Semi-mechanistic population pharmacokinetic modeling of DZIF-10c, a neutralizing antibody against SARS-Cov-2: predicting systemic and lung exposure following inhaled and intravenous administration.","authors":"Sree Kurup, Nieves Velez de Mendizabal, Stephan Becker, Erica Bolella, Dorothy De Sousa, Gerd Fätkenheuer, Henning Gruell, Florian Klein, Jakob J Malin, Ulrike Schmid, Julia Korell","doi":"10.1007/s10928-024-09947-2","DOIUrl":"10.1007/s10928-024-09947-2","url":null,"abstract":"<p><p>DZIF-10c (BI 767551) is a recombinant human monoclonal antibody of the IgG1 kappa isotype. It acts as a SARS-CoV-2 neutralizing antibody. DZIF-10c has been developed for both systemic exposure by intravenous infusion as well as for specific exposure to the respiratory tract by application as an inhaled aerosol generated by a nebulizer. An integrated preclinical/clinical semi-mechanistic population pharmacokinetic model was developed to characterize the exposure profile of DZIF-10c in the systemic circulation and lungs. To inform and reduce uncertainty around exposure in the lungs following different methods of dosing, preclinical cynomolgus monkey data was combined with human data using allometric scaling principles. Human serum concentrations of DZIF-10c from two clinical trials were combined with serum/plasma and lung epithelial lining fluid (ELF) concentrations from three preclinical studies to characterize the relationship between dosing, serum/plasma, and lung exposure. The final model was used to predict exposure in the lungs following different routes of administration. Simulations showed that inhalation provides immediate and relevant exposure in the lung ELF at a much lower dose compared with an infusion. Combining inhalation with intravenous therapy results in high and sustained DZIF-10c exposure in the lungs and systemic circulation, thereby combining the benefits of both routes of administration. By combining preclinical data with clinical data (via allometric scaling principles), the developed population pharmacokinetic model reduced uncertainty around exposure in the lungs allowing evaluation of alternative dosing strategies to achieve the desired concentrations of DZIF-10c in human lungs.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 1","pages":"3"},"PeriodicalIF":2.2,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cancer is one of the most common and fatal diseases worldwide and kills millions of people every year. Cancer drug resistance, lack of efficacy, and safety are significant problems in cancer patients. A multiorgan-on-a-chip (MOC) device consisting of breast and liver compartments was designed with AutoCAD software. The MOC molds were printed by a Formlabs Form 2 3D printer. MDA-MB-231, HepG2, and MCF-10 A cells were used for the MOC experiments. The cell lines were cultured at 37 °C with 5% CO2, and cell viability was assessed via Alamar blue dye to generate pharmacodynamics (PD) data. Drug concentrations from the cell culture media were analyzed via Agilent 1260 Infinity II HPLC with a Waters Symmetry C18 column and used to generate pharmacokinetics (PK) data. The PK and PD data were modeled and simulated by Monolix and Simulix software, respectively. The safety and efficacy of drug dosing regimens were compared, and the best dosing regimens were selected. This research designed and fabricated a unique MOC consisting of liver and breast compartments that overcomes the need for sealing or assembling. It was used for PK-PD modeling and simulations, and its functionality was proven experimentally. The new MOC will be helpful in preclinical trials to evaluate the efficacy and safety of drugs.
{"title":"Multiorgan-on-a-chip for cancer drug pharmacokinetics-pharmacodynamics (PK-PD) modeling and simulations.","authors":"Abdurehman Eshete Mohammed, Filiz Kurucaovalı, Devrim Pesen Okvur","doi":"10.1007/s10928-024-09955-2","DOIUrl":"10.1007/s10928-024-09955-2","url":null,"abstract":"<p><p>Cancer is one of the most common and fatal diseases worldwide and kills millions of people every year. Cancer drug resistance, lack of efficacy, and safety are significant problems in cancer patients. A multiorgan-on-a-chip (MOC) device consisting of breast and liver compartments was designed with AutoCAD software. The MOC molds were printed by a Formlabs Form 2 3D printer. MDA-MB-231, HepG2, and MCF-10 A cells were used for the MOC experiments. The cell lines were cultured at 37 °C with 5% CO<sub>2,</sub> and cell viability was assessed via Alamar blue dye to generate pharmacodynamics (PD) data. Drug concentrations from the cell culture media were analyzed via Agilent 1260 Infinity II HPLC with a Waters Symmetry C18 column and used to generate pharmacokinetics (PK) data. The PK and PD data were modeled and simulated by Monolix and Simulix software, respectively. The safety and efficacy of drug dosing regimens were compared, and the best dosing regimens were selected. This research designed and fabricated a unique MOC consisting of liver and breast compartments that overcomes the need for sealing or assembling. It was used for PK-PD modeling and simulations, and its functionality was proven experimentally. The new MOC will be helpful in preclinical trials to evaluate the efficacy and safety of drugs.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 1","pages":"1"},"PeriodicalIF":2.2,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142770065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-07-04DOI: 10.1007/s10928-024-09931-w
Alexander Janssen, Frank C Bennis, Marjon H Cnossen, Ron A A Mathôt
This work focusses on extending the deep compartment model (DCM) framework to the estimation of mixed-effects. By introducing random effects, model predictions can be personalized based on drug measurements, enabling the testing of different treatment schedules on an individual basis. The performance of classical first-order (FO and FOCE) and machine learning based variational inference (VI) algorithms were compared in a simulation study. In VI, posterior distributions of the random variables are approximated using variational distributions whose parameters can be directly optimized. We found that variational approximations estimated using the path derivative gradient estimator version of VI were highly accurate. Models fit on the simulated data set using the FO and VI objective functions gave similar results, with accurate predictions of both the population parameters and covariate effects. Contrastingly, models fit using FOCE depicted erratic behaviour during optimization, and resulting parameter estimates were inaccurate. Finally, we compared the performance of the methods on two real-world data sets of haemophilia A patients who received standard half-life factor VIII concentrates during prophylactic and perioperative settings. Again, models fit using FO and VI depicted similar results, although some models fit using FO presented divergent results. Again, models fit using FOCE were unstable. In conclusion, we show that mixed-effects estimation using the DCM is feasible. VI performs conditional estimation, which might lead to more accurate results in more complex models compared to the FO method.
这项工作的重点是将深隔室模型(DCM)框架扩展到混合效应的估算。通过引入随机效应,可以根据药物测量结果对模型预测进行个性化处理,从而对不同的治疗方案进行个体化测试。在一项模拟研究中,比较了经典的一阶算法(FO 和 FOCE)和基于机器学习的变异推理算法(VI)的性能。在变异推理中,随机变量的后验分布通过变异分布来近似,其参数可以直接优化。我们发现,使用路径导数梯度估计器版本的 VI 估算的变分近似值非常准确。在模拟数据集上使用 FO 和 VI 目标函数拟合的模型结果相似,都能准确预测群体参数和协变效应。相反,使用 FOCE 拟合的模型在优化过程中表现不稳定,得出的参数估计也不准确。最后,我们比较了这两种方法在两个真实世界数据集上的表现,这两个数据集是在预防和围手术期接受标准半衰期第八因子浓缩液治疗的 A 型血友病患者。同样,使用 FO 和 VI 拟合的模型显示了相似的结果,但使用 FO 拟合的一些模型显示了不同的结果。同样,使用 FOCE 拟合的模型也不稳定。总之,我们表明使用 DCM 进行混合效应估计是可行的。与 FO 方法相比,VI 可以进行条件估计,这可能会在更复杂的模型中得到更准确的结果。
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During the space travel mission, astronauts' physiological and psychological behavior will alter, and they will start consuming terrestrial drug products. However, factors such as microgravity, radiation exposure, temperature, humidity, strong vibrations, space debris, and other issues encountered, the drug product undergo instability This instability combined with physiological changes will affect the shelf life and diminish the pharmacokinetic and pharmacodynamic profile of the drug product. Consequently, the physicochemical changes will produce a toxic degradation product and a lesser potency dosage form which may result in reduced or no therapeutic action, so the astronaut consumes an additional dose to remain healthy. On long-duration missions like Mars, the drug product cannot be replaced, and the astronaut may relay on the available medications. Sometimes, radiation-induced impurities in the drug product will cause severe problems for the astronaut. So, this review article highlights the current state of various space-related factors affecting the drug product and provides a comprehensive summary of the physiological changes which primarly focus on absorption, distribution, metabolism, and excretion (ADME). Along with that, we insist some of the strategies like novel formulations, space medicine manufacturing from plants, and 3D printed medicine for astronauts in longer-duration missions. Such developments are anticipated to significantly contribute to new developments with applications in both human space exploration and on terrestrial healthcare.
在执行太空旅行任务期间,宇航员的生理和心理行为会发生变化,并开始服用地球上的药物产品。然而,由于受到微重力、辐射、温度、湿度、强烈振动、太空碎片等因素的影响,药物产品会出现不稳定的情况,这种不稳定性加上生理变化会影响药物产品的保质期,并降低药物产品的药代动力学和药效学特征。因此,理化变化将产生有毒的降解产物和药效较低的剂型,从而可能导致治疗作用降低或无效,因此宇航员需要额外服用剂量以保持健康。在火星等长时间飞行任务中,药物产品无法更换,宇航员只能依靠现有药物。有时,药物中由辐射引起的杂质会给宇航员带来严重问题。因此,这篇综述文章重点介绍了影响药物的各种太空相关因素的现状,并全面总结了主要集中在吸收、分布、代谢和排泄(ADME)方面的生理变化。此外,我们还坚持采用一些策略,如新型配方、利用植物制造太空药物,以及为执行更长时间任务的宇航员提供 3D 打印药物。预计这些发展将极大地促进人类太空探索和地面医疗保健应用的新发展。
{"title":"A review of the physiological effects of microgravity and innovative formulation for space travelers.","authors":"Jey Kumar Pachiyappan, Manali Patel, Parikshit Roychowdhury, Imrankhan Nizam, Raagul Seenivasan, Swathi Sudhakar, M R Jeyaprakash, Veera Venkata Satyanarayana Reddy Karri, Jayakumar Venkatesan, Priti Mehta, Sudhakar Kothandan, Indhumathi Thirugnanasambandham, Gowthamarajan Kuppusamy","doi":"10.1007/s10928-024-09938-3","DOIUrl":"10.1007/s10928-024-09938-3","url":null,"abstract":"<p><p>During the space travel mission, astronauts' physiological and psychological behavior will alter, and they will start consuming terrestrial drug products. However, factors such as microgravity, radiation exposure, temperature, humidity, strong vibrations, space debris, and other issues encountered, the drug product undergo instability This instability combined with physiological changes will affect the shelf life and diminish the pharmacokinetic and pharmacodynamic profile of the drug product. Consequently, the physicochemical changes will produce a toxic degradation product and a lesser potency dosage form which may result in reduced or no therapeutic action, so the astronaut consumes an additional dose to remain healthy. On long-duration missions like Mars, the drug product cannot be replaced, and the astronaut may relay on the available medications. Sometimes, radiation-induced impurities in the drug product will cause severe problems for the astronaut. So, this review article highlights the current state of various space-related factors affecting the drug product and provides a comprehensive summary of the physiological changes which primarly focus on absorption, distribution, metabolism, and excretion (ADME). Along with that, we insist some of the strategies like novel formulations, space medicine manufacturing from plants, and 3D printed medicine for astronauts in longer-duration missions. Such developments are anticipated to significantly contribute to new developments with applications in both human space exploration and on terrestrial healthcare.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"605-620"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-10-08DOI: 10.1007/s10928-024-09940-9
Freya Bachmann, Gilbert Koch, Robert J Bauer, Britta Steffens, Gabor Szinnai, Marc Pfister, Johannes Schropp
Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal drug doses for any pharmacometrics model for a given dosing scenario. In the present work, we enhance the OptiDose concept to compute optimal drug dosing with respect to both efficacy and safety targets. Usually, these are not of equal importance, but one is a top priority, that needs to be satisfied, whereas the other is a secondary target and should be achieved as good as possible without failing the top priority target. Mathematically, this leads to state-constrained optimal control problems. In this paper, we elaborate how to set up such problems and transform them into classical unconstrained optimal control problems which can be solved in NONMEM. Three different optimal dosing tasks illustrate the impact of the proposed enhanced OptiDose method.
{"title":"Computing optimal drug dosing regarding efficacy and safety: the enhanced OptiDose method in NONMEM.","authors":"Freya Bachmann, Gilbert Koch, Robert J Bauer, Britta Steffens, Gabor Szinnai, Marc Pfister, Johannes Schropp","doi":"10.1007/s10928-024-09940-9","DOIUrl":"10.1007/s10928-024-09940-9","url":null,"abstract":"<p><p>Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal drug doses for any pharmacometrics model for a given dosing scenario. In the present work, we enhance the OptiDose concept to compute optimal drug dosing with respect to both efficacy and safety targets. Usually, these are not of equal importance, but one is a top priority, that needs to be satisfied, whereas the other is a secondary target and should be achieved as good as possible without failing the top priority target. Mathematically, this leads to state-constrained optimal control problems. In this paper, we elaborate how to set up such problems and transform them into classical unconstrained optimal control problems which can be solved in NONMEM. Three different optimal dosing tasks illustrate the impact of the proposed enhanced OptiDose method.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"919-934"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142391398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}