Abstract:Gemcitabine is a nucleoside analog effective against several solid tumors. Standard treatment consists of an intravenous infusion over 30 min. This is an invasive, uncomfortable and often painful method, involving recurring visits to the hospital and costs associated with medical staff and equipment. Gemcitabine’s activity is significantly limited by numerous factors, including metabolic inactivation, rapid systemic clearance of gemcitabine and transporter deficiency-associated resistance. As such, there have … Show more
“…For instance, a gemcitabine PBPK model was developed using Gastroplus software with IV and oral routes of administration to evaluate the plasma-concentration time profile. The author showed that the drug Cmax was lower, but AUC (area under curve) was higher for the tablet dosage regimens (1000 mg, three/day and 1500 mg 2–3 times a day) compared to the IV infusion [ 47 ]. Often, the availability of the compound in systemic circulation is dependent on multiple factors.…”
Section: Unravelling the Art Of Developing Pbpk Modelsmentioning
confidence: 99%
“…The inhalation route is quite successful in respiratory diseases and also for rapid systemic drug delivery. A PBPK model was built for inhaled nemirasalib, consisting of extra-thoracic, thoracic, bronchiolar and alveolar tissues for evaluating pulmonary drug absorption [ 47 ]. For environmental chemical such as xylene, PBPK with lungs as a route for inhalation and exhalation was modeled for reconstructing the human exposure [ 52 ].…”
Section: Unravelling the Art Of Developing Pbpk Modelsmentioning
Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration–time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.
“…For instance, a gemcitabine PBPK model was developed using Gastroplus software with IV and oral routes of administration to evaluate the plasma-concentration time profile. The author showed that the drug Cmax was lower, but AUC (area under curve) was higher for the tablet dosage regimens (1000 mg, three/day and 1500 mg 2–3 times a day) compared to the IV infusion [ 47 ]. Often, the availability of the compound in systemic circulation is dependent on multiple factors.…”
Section: Unravelling the Art Of Developing Pbpk Modelsmentioning
confidence: 99%
“…The inhalation route is quite successful in respiratory diseases and also for rapid systemic drug delivery. A PBPK model was built for inhaled nemirasalib, consisting of extra-thoracic, thoracic, bronchiolar and alveolar tissues for evaluating pulmonary drug absorption [ 47 ]. For environmental chemical such as xylene, PBPK with lungs as a route for inhalation and exhalation was modeled for reconstructing the human exposure [ 52 ].…”
Section: Unravelling the Art Of Developing Pbpk Modelsmentioning
Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration–time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.
“…For instance, gemcitabine PBPK model was developed using Gastroplus software with IV and oral route of administration to evaluate plasma-concentration time profile. Author showed that the drug Cmax was lower, but AUC was higher for the tablet dosage regimens (1000 mg, three*/day and 1500 mg 2-3*/day) compared to the IV infusion (Ferreira et al, 2021) Often the availability of the compound in systemic circulation is dependent on multiple factors. For instance, absorption is often considered the critical parameter for oral route along with influence from fasting or fed state.…”
Section: Route Of Administrationmentioning
confidence: 99%
“…Inhalation route of exposure is quite successful in respiratory diseases and also for rapid systemic drug delivery. PBPK model was built for inhaled nemirasalib consisting of extra-thoracic, thoracic, bronchiolar and alveolar tissues for evaluating pulmonary drug absorption (Ferreira et al, 2021). For environmental chemical like xylene, PBPK with lungs as a route for inhalation and exhalation was modeled for reconstructing the human exposure (McNally et al, 2012).…”
Physiologically Based Pharmacokinetic Models (PBPK) are mechanistical tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognised by regulatory authorities for predicting organ concentration-time profile, pharmacokinetic and daily intake dose of xenobiotics. Extension of PBPK models to capture sensitive populations like pediatric, geriatric, pregnant females, fetus etc. and diseased population like renal impairment, liver cirrhosis etc. is a must. However, the current modelling practice and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology, and calculation of biochemical parameters for integrating the knowledge and refining existing PBPK models. Specific PBPK covering compartments like cerebrospinal fluid, and hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints like developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in-silico models where experimental data is unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building qAOPs, use of machine learning for improving existing models along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.
“…The generic GastroPlus ® ACAT model provided reasonable predictions especially for biopharmaceutical classification system (BCS) class 1 compounds [20]. In addition, the ability of PBPK models to predict oral PK will also improve, providing a better tool for the discovery and development of new medicines [21][22][23][24], as drug combinations or drug synergism.…”
Chemotherapy is the main treatment for most early-stage cancers; nevertheless, its efficacy is usually limited by drug resistance, toxicity, and tumor heterogeneity. Cell-penetrating peptides (CPPs) are small peptide sequences that can be used to increase the delivery rate of chemotherapeutic drugs to the tumor site, therefore contributing to overcoming these problems and enhancing the efficacy of chemotherapy. The drug combination is another promising strategy to overcome the aforementioned problems since the combined drugs can synergize through interconnected biological processes and target different pathways simultaneously. Here, we hypothesized that different peptides (P1–P4) could be used to enhance the delivery of chemotherapeutic agents into three different cancer cells (HT-29, MCF-7, and PC-3). In silico studies were performed to simulate the pharmacokinetic (PK) parameters of each peptide and antineoplastic agent to help predict synergistic interactions in vitro. These simulations predicted peptides P2–P4 to have higher bioavailability and lower Tmax, as well as the chemotherapeutic agent 5-fluorouracil (5-FU) to have enhanced permeability properties over other antineoplastic agents, with P3 having prominent accumulation in the colon. In vitro studies were then performed to evaluate the combination of each peptide with the chemotherapeutic agents as well as to assess the nature of drug interactions through the quantification of the Combination Index (CI). Our findings in MCF-7 and PC-3 cancer cells demonstrated that the combination of these peptides with paclitaxel (PTX) and doxorubicin (DOXO), respectively, is not advantageous over a single treatment with the chemotherapeutic agent. In the case of HT-29 colorectal cancer cells, the combination of P2–P4 with 5-FU resulted in synergistic cytotoxic effects, as predicted by the in silico simulations. Taken together, these findings demonstrate that these CPP6-conjugates can be used as adjuvant agents to increase the delivery of 5-FU into HT-29 colorectal cancer cells. Moreover, these results support the use of in silico approaches for the prediction of the interaction between drugs in combination therapy for cancer.
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