Problems of emerging antibiotic resistance are becoming a serious threat worldwide, and at the same time, the interest to develop new antimicrobials has declined. There is consequently a need for efficient methods to develop new treatments that minimize the risk of resistance development and that are effective on infections caused by resistant strains. Based on in silico mathematical models, describing the time course of exposure (Pharmacokinetics, PK) and effect (Pharmacodynamics, PD) of a drug, information can be collected and the outcome of various exposures may be predicted. A general model structure, that characterizes the most important features of the system, has advantages as it can be used for different situations. The aim of this thesis was to develop Pharmacokinetic-Pharmacodynamic (PKPD) models describing the bacterial growth and killing after mono- and combination exposures to antibiotics and to explore the predictive ability of PKPD-models across preclinical experimental systems. Models were evaluated on data from other experimental settings, including prediction into animals. A PKPD model characterizing the growth and killing for a range of E. coli bacteria strains, with different MICs, as well as emergence of resistance, was developed.  The PKPD model was able to predict results from different experimental conditions including high start inoculum experiments, a range of laboratory and clinical strains as well as experiments where wild-type and mutant bacteria are competing at different drug concentrations. A PKPD model, developed based on in vitro data, was also illustrated to have the capability to replicate the data from an in vivo study. This thesis illustrates the potential of PKPD models to characterize in vitro data and their usage for predictions of different types of experiments. The thesis supports the use of PKPD models to facilitate development of new drugs and to improve the use of existing antibiotics.

<p>Despite advancements in therapies, such as surgery, irradiation (IR) and chemotherapy, outcome for patients suffering from glioblastoma (GBM) remains fatal; the median survival time is only about 15 months. Even with novel therapeutic targets, networks and signaling pathways being discovered, monotherapy with such agents targeting such pathways has been disappointing in clinical trials. Poor prognosis for GBM can be attributed to several factors, including failure of drugs to cross the blood-brain-barrier (BBB), tumor heterogeneity, invasiveness, and angiogenesis. Development of tumor resistance, particularly to temozolomide (TMZ) and IR, creates a substantial clinical challenge.</p><p> </p><p>The primary focus of the work described herein was to develop a modeling and simulation approach that could be applied to rationally develop novel combination therapies and dose regimens that mitigate resistance development. Specifically, TMZ was combined with small molecule inhibitors that are either currently in clinical trials or are approved drugs for other cancer types, and which target the disease at various resistance signaling pathways that are induced in response to TMZ monotherapy. To accomplish this objective, an integrated PKPD modeling approach was used. A PK model for each drug was first defined. PK models were subsequently linked to a PD model description of tumor growth dynamics in the presence of a single drug or combinations of drugs. A key outcome of these combined PKPD models was tumor static concentration (TSC) curves of TMZ in combination with small molecule inhibitors that identify combination drug exposures predicted to arrest tumor growth. This approach was applied to TMZ in combination with abemaciclib (a dual CDK4/6 small molecule inhibitor) based on data from a published study evaluating abemaciclib (ACB) efficacy in combination with TMZ in a U87 GBM xenograft model. TSC was also constructed for TMZ in combination with RG7388 (MDM2 inhibitor) based on the data from an in-vivo study that evaluated effects on tumor growth suppression of these small molecule inhibitors in combination with TMZ in GBM 10 patient derived xenografts.</p><p>In GBM 43 mouse xenografts, emergence of resistance to TMZ treatment was identified. Thus, a resistance integrated PKPD model was developed to predict tumor growth kinetics after treatment with TMZ in GBM 43 tumors. Population PK models in immune deficient NOD.Cg-<em>Prkdc^scid Il2rg^tm1Wjl</em>/SzJ (NSG) mice for TMZ and small molecule inhibitors (GDC0068/RG7112) were developed based on a combination of data obtained from an in-vivo study and published sources. Subsequently, PK models were linked to tumor volume data obtained from GBM 43 subcutaneous xenografts. Model parameters quantifying tumor volume dynamics were precisely estimated (coefficient of variation < 40%) compared to a base tumor growth inhibition model in GBM 43 that did not incorporate resistance development. Graphical diagnostics of the resistance incorporated PKPD tumor growth inhibition model demonstrated a superior fit compared to the base model, and accurately captured the emergence of resistance to the TMZ monotherapy treatment observed in the GBM 43 patient derived xenograft model.</p>