Cabozantinib enhances the response of NSCLC cells with wild-type EGFR to erlotinib and pharmacodynamic modeling of their sequential combinations

The epidermal growth factor receptor（EGFR）—tyrosine kinase inhibitors（TKIs） monotherapies have limited efficacy in the treatment of EGFR mutation-negative non-small cell lung cancers（NSCLCs）. In the present study, we aimed to investigate the combined effect of erlotinib（ER） and cabozantinib（CAB） on NSCLC cell lines harboring wild-type EGFR and to optimize the dosage regimens using pharmacodynamic（PD） modeling and simulation. Therefore, we examined the combined effect of ER and CAB on cell viability, cloning, apoptosis induction, migration and growth dynamics in H1299 and A549 cells. PD modeling and simulation were also performed to quantitatively describe the H1299 cells growth dynamics and to optimize the dosage regimens as well. Our results showed that CAB effectively enhanced the sensitivity of both cell lines to ER. The PD models fitted the data well, and some important parameters were obtained. The exponential（λ_0） and linear（λ_1） growth rates of H1299 cells were 0.0241 h^（–1） and 360 cells？h^（–1）, respectively. The Emax of ER and CAB was 0.0091 h^（–1） and 0.0085 h^（–1）, and the EC50 was 0.812 μM and 1.16 μM, respectively. The synergistic effect observed in the experiments was further confirmed by the estimated combination index φ（1.37）,（95% confidence interval： 1.24–1.50）, obtained from PD modeling. Furthermore, the dosage regimens were optimized using simulations. In summary, both the experimental and modeling results demonstrated the synergistic interaction between ER and CAB in NSCLCs without EGFR mutations. Sequential combinations of ER and CAB provided an option for the therapy of the NSCLCs with wild-type EGFR, which would provide some references for preclinical study and translational research as well.

Propofol Target-Controlled Infusion Modeling in Rabbits:Pharmacokinetic and Pharmacodynamic Analysis

This study aimed to establish a new propofol target-controlled infusion（TCI） model in animals so as to study the general anesthetic mechanism at multi-levels in vivo. Twenty Japanese white rabbits were enrolled and propofol（10 mg/kg） was administrated intravenously. Artery blood samples were collected at various time points after injection, and plasma concentrations of propofol were measured. Pharmacokinetic modeling was performed using Win Nonlin software. Propofol TCI within the acquired parameters integrated was conducted to achieve different anesthetic depths in rabbits, monitored by narcotrend. The pharmacodynamics was analyzed using a sigmoidal inhibitory maximal effect model for narcotrend index（NI） versus effect-site concentration. The results showed the pharmacokinetics of propofol in Japanese white rabbits was best described by a two-compartment model. The target plasma concentrations of propofol required at light anesthetic depth was 9.77±0.23 μg/m L, while 12.52±0.69 μg/m L at deep anesthetic depth. NI was 76.17±4.25 at light anesthetic depth, while 27.41±5.77 at deep anesthetic depth. The effect-site elimination rate constant（ke0） was 0.263/min, and the propofol dose required to achieve a 50% decrease in the NI value from baseline was 11.19 μg/m L（95% CI, 10.25–13.67）. Our results established a new propofol TCI animal model and proved the model controlled the anesthetic depth accurately and stably in rabbits. The study provides a powerful method for exploring general anesthetic mechanisms at different anesthetic depths in vivo.

A pharmacodynamic model of portal hypertension in isolated perfused rat liver

AIM： To develop a pharmacodynamic model of porta hypertension from chronic hepatitis. METHODS： Pathological changes and collagen depositions were analyzed using morphometry to confirm CCI4-induced chronic hepatitis. At do, d28, ds6 and d84 of the process, the portal perfused velocities （μL/min） in isolated rat livers were exactly controlled with a quanti-fied pump. The pressure （mmHg） was monitored with a Physiological System. The geometric concentrations of phenylephrine or acetylcholine were added to a fixed volume （300 mL） of the circulating perfusate. The equation, the median effective concentration and its 95% confidence intervals of phenylephrine or acetyl- choline were regressed with Prism-4 software in non-linear fit and various slopes. In the isolated perfused rat livers with chronic hepatitis, both median effective concentrations were defined as the pharmacodynamic model of portal hypertension.CONCLUSION： A pharmacodynamic model of portal hypertension in isolated perfused rat livers with chronic hepatitis was defined as the median effective concen- trations of phenylephrine and acetylcholine.

Pharmacodynamic model of dopamine D1 receptor agonists in the treatment of breast cancer lung metastasis

Previous study has shown that dopamine D1 receptor(D1DR)agonists,fenoldopam(FEN)and l-stepholidine(l-SPD),have inhibitory effects on breast cancer lung metastasis.To quantitatively describe and predict the pharmacodynamic(PD)properties of FEN and l-SPD and to explore the PD model structure of cancer metastasis treating drugs,we used the data of lung metastasis in 4T1 breast cancer mice under the treatment of either FEN or l-SPD,and established a PD model.The PD model assumed an exponential growth for both primary tumor and metastasis.The primary tumor emitted cells to form metastases,and the cell emitting rate was proportional to power form of the primary tumor weight.The total number of lung metastasis was set as the target value.D1DR agonists inhibited metastasis by inhibiting cell emitting rate instead of the growth rate of primary tumor or metastasis.The model results showed that the decrease in the number of lung metastases was roughly proportional to the square of the drug dose.The values of PD coefficient reflected the inhibitory ability of the drugs,and that of l-SPD(0.274 kg/mg)was greater than that of FEN(0.0393 kg/mg).This PD model can quantitatively describe the effects of FEN and l-SPD on the progression of lung metastasis in 4T1 primary breast cancer mice and can predict the time course of drug efficacy at multiple doses,providing a reference for PD model structure of other drugs for cancer metastasis indication.

Genome-scale metabolic modeling in antimicrobial pharmacology

The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades.This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant micro-bial pathogens.However,the detailed mechanisms of action,resistance,and toxicity of many antimicrobials remain uncertain,significantly hampering the development of novel antimicrobials.Genome-scale metabolic model(GSMM)has been increasingly employed to investigate microbial metabolism.In this review,we discuss the latest progress of GSMM in antimicrobial pharmacology,particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity,resistance,and toxicity.We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities(e.g.,gene expression),identi-fication of potential drug targets,and integration with machine learning and pharmacokinetic/pharmacodynamic modeling.Overall,GSMM has significant potential in elucidating the critical role of metabolic changes in antimi-crobial pharmacology,providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.