Date of Award


Document Type


Degree Name

Doctor of Pharmaceutical Sciences


Pharmaceutical Sciences

First Advisor

David Taft

Committee Chair and Members

David Taft

Jaclyn Cusumano

Kushal Shah

Rutesh Dave


This thesis focuses on combating antibacterial resistance by developing novel in vitro and in silico techniques. In vitro techniques such as in vitro pharmacodynamic (IVPD) modeling are powerful tools for investigating pharmacokinetic and pharmacodynamic response of antibiotics against bacteria. The standard IVPD model in the literature works for simulating monotherapy and combination therapy of drugs having similar half live. But it does not work for combination therapy of drugs having different half live. The method present in the literature for combination therapy of drugs with different half live was described by Blaser. By utilizing Blaser’s method, it was observed that the concentration of drug having a longer half-life could not be achieved as expected in vivo. Therefore, it was essential to develop a novel in vitro pharmacodynamic model to address this limitation. The novel IVPD model in this thesis has overcome this issue by varying the infusion rate at which the drug with longer half-life was being supplemented to the central vessel. The change in infusion rate was calculated to mimic the in vivo plasma concentration of the longer half-life drug. The novel IVPD model was verified by running a 48 hour experiment where the concentration of drug with longer half-life (ceftriaxone) was monitored.

Another aspect of this research was dedicated to developing a physiologically based pharmacokinetic and pharmacodynamic (PBPK-PD) model for combination therapy of amicrobial medications acting synergistically (ampicillin and ceftriaxone). PBPK modeling is a dynamic method that predicts in vivo systemic drug exposure in humans based on the compound’s physicochemical properties and absorption, distribution, metabolism and excretion (ADME) characteristics. Interlinking it with the pharmacodynamic model would help to understand the change in pharmacodynamic response caused due to alterations in the pharmacokinetics of drug that impact systemic exposure. An advantage of developing PBPK-PD model for combination therapy is it can act as a predictive tool to optimize dosing regimen and understand the pharmacodynamic response in special populations (renal impaired patients, pediatrics, pregnant women, etc.).

To develop the PBPK-PD model, substrate profiles for ampicillin and ceftriaxone were first created and verified in healthy volunteers against published literature. Verification was performed by visual predictive check and by calculating the fold error for maximum concentration (Cmax) and area under the curve (AUC). A custom PD model was developed using lua script code which can simulate a pharmacodynamic response for drugs acting synergistically. The PBPK model was interlinked with the PD model. The PBPK-PD model was verified against in vitro results published in the literature. The PD end point was the observed decrease in bacterial count over a period of 72 hours. A dosing regimen of ampicillin 2g q 4 hours and ceftriaxone 2g q 12 hours was simulated using the PBPK-PD model. It was observed that the PBPK-PD model developed in this research could capture the in vitro pharmacodynamic experiment data.

Once verified, the PBPK-PD model was extended to a population of severe renal impaired patients. PBPK-PD model was used to justify the change in dose frequency of ampicillin when given in combination with ceftriaxone in severe renal impaired patients’ population. Two dosing regimens were simulated in severe renal impaired patients: 1) ampicillin 2g q 8 hours and ceftriaxone 2g q 12 hours, and 2) ampicillin 2g q 6 hours and ceftriaxone 2g q 12 hours.

In a patient population with renal impairment a regimen comprised of ampicillin IV 2000 mg every q-6 hours and ceftriaxone IV 2000 mg q-12 hours achieved complete eradication of bacteria. The novel PBPK-PD model created in this dissertation research is of clinical significance as an in silico approach can be used to optimizing dosing regimens in special patient populations being treated with a combination of antimicrobial drugs acting synergistically.