In oncology, there is a need to optimize drug treatment for efficient eradication of tumors, minimization of adverse effects (AEs), and prolonging patient survival. Pharmacometric models can be developed to streamline information between drug development phases, describe and quantify response to treatment, and determine dose regimens that balance toxicity and efficacy. In this thesis, data from trastuzumab emtansine (T-DM1) and taxane drug treatment were used to develop pharmacometric models of pharmacokinetics (PK), AEs, anti-tumor response, and survival, supporting drug development. T-DM1 is an antibody-drug conjugate (ADC) for treatment of human epidermal growth factor receptor 2 (HER2)–positive breast cancer. ADCs are a relatively new class of oncologic agents, and contain multiple drug-to-antibody ratio (DAR) moieties in their dose product. The complex distribution of T-DM1 was elucidated through PK models developed using in vitro and in vivo rat and cynomolgus monkey DAR data. Mechanism–based PK/pharmacodynamic (PKPD) models were also developed for T-DM1 that described the AEs thrombocytopenia (TCP) and hepatotoxicity in patients receiving T-DM1. Variable patterns of platelet and transaminase (ALT and AST) response were quantified, including an effect of Asian ethnicity that was related to higher incidences of TCP.  Model simulations, comparing dose intensities (DI) and Grade 3/4 incidences between the approved T-DM1 dose (3.6 mg/kg every three weeks) and weekly regimens, determined that 2.4 mg/kg weekly provided the highest DI. Docetaxel and paclitaxel are taxane treatment options for HER2–negative breast cancer. Tumor response data from these treatments were used to develop a mechanism–based model of tumor quiescence and drug–resistance. Subsequently, a parametric survival analysis found that tumor baseline and the model–predicted time to tumor growth (TTG) were predictors of overall survival (OS). This tumor and OS modeling approach can be applied to other anticancer treatments with similar patterns of drug–resistance. Overall, the pharmacometric models developed within this thesis present new modeling approaches and provide understanding on ADC PK and PKPD (TCP and hepatotoxicity), as well as drug–resistance tumor response. These models can inform simulation strategies and clinical study design, and be applied towards dose finding for anticancer drugs in development, especially ADCs.

Biologic therapies have revolutionized the treatment of rheumatoid arthritis, a common chronic inflammatory disease, mainly characterized by the chronic inflammation of the joints. The activity and progression of the disease are highly variable, both between subjects and between the successive assessments for the same subject. Standardized assessments of clinical variables have been developed to reflect the disease activity and evaluate new therapies. Pharmacokinetics-pharmacodynamic (PKPD) models and methods for analyzing the generated time-course data are needed to improve the interpretation of the clinical trials’ outcomes, and to describe the variability between subjects, including patients characteristics, disease factors and the use of concomitant treatments that may affect the response to treatment. In addition, good simulation properties are also desirable for predicting clinical responses for various populations or for different dosing schedules. The aim of this thesis was to develop methods and models for analyzing pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) data from rheumatoid arthritis patients, illustrated by treatment with a new anti-TNFα biologic drug under clinical development, certolizumab pegol. Two models were developed that characterized the relationship between the exposure to the drug and the efficacy ACR variables that represent improvement of the disease; a logistic-type Markov model for 20% improvement (ACR20) and a continuous-type Markov model for simultaneous analysis of 20% (ACR20), 50% (ACR50) and 70% (ACR70) improvement. Both models accounted for the within-subjects correlation in the successive clinical assessments and were able to capture the observed ACR responses over time. Simulations from these models of the ACR20 response rate supported dosing regimens of 400 mg at weeks 0, 2 and 4 to achieve a rapid onset of response to the treatment, followed by 200 mg every 2 weeks, or alternative maintenance regimen of 400 mg every 4 weeks. The immunogenicity induced by the biologic drug was characterized by a time to event model describing the time to appearance of antibodies directed against the drug. The immunogenicity was predicted to appear mainly during the first 3 months following the start of the treatment and to be reduced at higher trough concentrations of CZP, as well as with concomitant administration of MTX. The full time-course of sequential events, such as dose-exposure-efficacy relations, is most accurately described by a simultaneous analysis of all data. However, due to the complexity and runtime limitations of such an analysis, alternatives are often used. In this thesis, a method, IPPSE, was developed and compared to the reference simultaneous method and to existing alternative methods. The IPPSE method was shown to provide accuracy and precision of estimates similar to the simultaneous method, but with easier implementation and shorter run times. In conclusion, two PKPD models and one immunogenicity model were developed for evaluation of the response of a biologic drug against rheumatoid arthritis that allowed accurate analysis and simulation of clinical trial data, as well as serving as examples for how a model-informed basis for decisions about biological drugs can be created.

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.

As multidrug resistance in Gram-negative bacilli increases, the old antibiotic colistin has rapidly gained attention as one of few last line treatment options in the form of colistin methanesulfonate (CMS), which is hydrolyzed to colistin both in vitro and in vivo. There is a dearth of knowledge on fundamental aspects of colistin, including pharmacokinetics and optimal dosing regimens. The aim of this thesis was to improve the basis for optimal colistin therapy. To be able to study colistin, an LC-MS/MS assay method was developed which is sensitive, specific and useful in both in vivo and in vitro studies. Using this method we detected a significant loss of colistin during standard laboratory procedures. This loss was characterized and quantified, the hypothesis being that the loss is mainly caused by adsorption to labware. The pharmacokinetics of colistin was studied in two populations of critically ill patients, one with normal renal function and one with renal replacement therapy. Plasma concentrations were assayed with the method above, and population modeling was employed to describe the data. The results include a previously unseen, long elimination half-life of colistin. The data from the population on renal replacement therapy was described without modeling, and showed that both CMS and colistin are cleared by hemodiafiltration. Combination therapy is an approach that is often used when treating patients infected with multidrug-resistant pathogens. The thesis discusses how the joint effect of antibiotics can be measured using colistin and meropenem as a model, and proposes a method for testing antibiotic combinations. Furthermore, a PKPD model was adapted to describe the pharmacodynamics of the combination. In conclusion, a specific and sensitive method for analysis of colistin was developed and the adsorption of colistin to materials was described. The assay method has been well accepted internationally. The pharmacokinetics of colistin and CMS was described in two important patient populations, partly with surprising results that have influenced dosages of colistin worldwide. The pharmacodynamics of combination therapy was investigated and quantified, and the methods applied could be further developed into clinically useful tools for selection of antibiotic combinations.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-09-19T19:36:44Z No. of bitstreams: 1 MiguelAdelinoDaSilvaFilho_TESE.pdf: 3307229 bytes, checksum: 121783886d066fa57c0f37b4ba45bf1e (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-09-19T22:27:31Z (GMT) No. of bitstreams: 1 MiguelAdelinoDaSilvaFilho_TESE.pdf: 3307229 bytes, checksum: 121783886d066fa57c0f37b4ba45bf1e (MD5) / Made available in DSpace on 2017-09-19T22:27:31Z (GMT). No. of bitstreams: 1 MiguelAdelinoDaSilvaFilho_TESE.pdf: 3307229 bytes, checksum: 121783886d066fa57c0f37b4ba45bf1e (MD5) Previous issue date: 2017-05-19 / O aumento da incid?ncia das infec??es f?ngicas sist?micas juntamente com o crescimento de isolados cl?nicos resistente ao tratamento usual ? um cen?rio epidemiol?gico ? desafiador para a pr?tica cl?nica. O desenvolvimento de novas estrat?gias terap?uticas ? de suma import?ncia para o controle desse quadro epidemiol?gico. A anfotericina B micelar (AmB-D) apresenta um satisfat?rio perfil de atividade, por?m sua toxicidade ? pronunciada. Uma alternativa para redu??o da sua toxicidade ? o aquecimento moderado da AmB-D que resulta nos superagregados (AmB-H). Objetivando desenvolver uma nova alternativa terap?utica, foi avaliado as modifica??es estruturais da AmB-H acessando suas caracter?sticas f?sico-qu?micas, perfil de toxicidade ?in vitro? em hem?cias, atividade ?in vitro? em cepas de Candida sp e o delineamento de um modelo semi mecan?stico PKPD. A espectroscopia demonstrou que h? um deslocamento da banda dos agregados de 327nm para 323nm, AmB-D e AmB-H respectivamente. O estudo de dilui??o comprovou que AmB-H atua como reservat?rio das formas monom?ricas de anfotercinida B. Os estudos de toxicidade em hem?cias demonstraram que as formas agregadas de AmB-H em altas concentra??es (5mg.L-1 e 50mg.L-1) apresenta em torno de 2% de libera??o de hemoglobina enquanto que a AmB-D apresenta libera??o de 100%, comprovando o perfil de baixa toxicidade por um modelo mais robusto. A avalia??o de atividade da AmB-H apresentou resultados similares aos da AmB-D em ensaios com C. albicans e C. parapsiloses. A avalia??o das etapas cr?ticas do processo de liofiliza??o de AmB-H demonstrou ser um procedimento seguro, o que torna uma excelente estrat?gia de produ??o dos superagregados. Com rela??o ? farmacometria, o modelo semi mecan?stico PKPD demonstrou que AmB-H ? cerca de 25% mais potente que a AmB-D e tamb?m forneceu outros importantes dados quantitativos que corroboram com a literatura cient?fica e que servir?o de suporte tanto para o desenvolvimento de estudos cl?nicos. Todos esses dados demonstram que a AmB-H ? um sistema promissor e juntamente com outros dados da literatura, fornece subs?dios cient?ficos s?lidos para dar in?cio aos estudos cl?nicos e finalmente o desenvolvimento de uma nova estrat?gia terap?utica. / The increasing incidence of systemic fungal infections along with the growth of clinical isolates resistant to first line clinical treatment is a challenging epidemiological scenario in clinical practice. The development of new strategies is of paramount importance for the control of this rampant problem. Micellar amphotericin B (AmB-D) has a satisfactory activity profile, but its toxicity is pronounced. An alternative to reduce its toxicity is the controled heating of AmB-D resulting in superaggregates (AmB-H). Aiming a new therapeutic alternative, it was evaluated as structural modifications of AmB-H by accessing its physico-chemical characteristics, ?in vitro? toxicity profile in red blood cells, ?in vitro? activity in Candida sp strains and designed a semi-mechanistic PKPD model. A spectroscopy screening showed that there is a blue shift at the aggregate band from 327nm to 323nm, AmB-D and AmB-H respectively. The dilution study showed that AmB-H acts as the reservoir for the amphotericin B monomeric forms. Red blood cell toxicity studies have demonstrated that at high concentrations of AmB-H (5mg.L-1 and 50mg.L-1) presents around 2% of hemoglobin leakage whereas AmB-D presents the 100%, proving the profile of low toxicity by this experiment model. The activity evaluation, the AmB-H presented similar results to AmB-D against C. albicans and C. parapsiloses. The evaluation of the critical steps of the freeze-drying process, AmB-H has proven no changing during the process making it a good manufacturing strategy. Regarding the pharmacometrics, the semi-mechanistic PKPD model demonstrated that AmB-H is about 25% more potent than AmB-D and the model also provides quantitative parameters estimates that corroborate with a scientific literature and it provides a support to the development of clinical trials. All these data demonstrate that AmB-H is a promising system and together with scientific literature, it produces solid scientific subsidies to initiate clinical trials and finally the development of a new therapeutic strategy.

Resistant bacteria are becoming more and more of a problem but treatment with antibiotics in combination may overcome and prevent resistance development. The combination of meropenem and colistin against Pseudomonas aeruginosa has been proposed as a promising treatment to increase the bactericidal effect and a synergistic effect has been proposed. A Pharmacokinetic-Parmacodynamic (PKPD) model that describes the dynamics of bacteria kill could be used to evaluate if the effects are additive or not. The model could later also be used to find optimal dosing for both of the antibiotics used alone or in combination with each other. The aim of the present study was to develop a PKPD model that describes the bactericidal activity of the two antibiotics, both in mono-therapy and in combination. The data were from in vitro static time kill-curve experiments that had been conducted on two strains of Pseudomonas aeruginosa; the wild-type (ATCC 27853) and the resistant-type (PL0603761). Resistance was observed in the experimental data and thus it had to be taken into account in the modelling. PKPD models were fitted to the bacterial counts in NONMEM with pharmacodynamic compartments for susceptible and resting bacteria. In the resting compartment the bacteria could not be killed. The bacteria moved into the resting compartment from the susceptible compartment when a certain concentration of bacteria was obtained. A pharmacokinetic compartment characterized changes in drug concentrations and the drug degradation during the experimental time was considered. Two different drug effects were tried on the susceptible bacteria, linear effect and Emax models.. The resistance development occurring during the experiments was described by two compartments where the parameter kon determined the rate of onset of resistance development. In the final model, kon was found to either be concentration-independent or dependent, depending on antibiotics and bacteria. The degree of resistance development produced an overall inhibitory effect on the drug effect. The growth rate was estimated to be lower and the EC50 to be higher for the resistant compared to the wild-type bacteria. The model was used to predict the expected time-kill curve if the effect of the two drugs are additive when combining the two drugs. The observed  bacteria kill was lower than the model predicted for the wild-type bacteria. For the resistant bacteria the assumption of additive bacteria kill for the two drugs-seemed adequate.

Pharmacokinetic (PK)- and pharmacodynamic (PD) modeling are useful tools whenassessing treatment effect. A patient’s adherence can potentially be rate-limiting, since it isthe first process in a chain of processes that determines treatment effect. Therefore agreater system taking into consideration PKPD as well as adherence models couldpotentially unlock a greater system understanding. This study focuses on investigating thefeasibility of combining models concerning adherence, PK and PD. An extensive mapping of previously made work on the topics of PKPD model developmentand adherence models concerning type 2 diabetes was conducted. Results concluded thatthere are gaps in research regarding adequate adherence-scoring methods that easily can belinked to dosing regimens. Furthermore, there is lacking research regarding feedback fromexposure-response to adherence. A simple model was implemented to provide a proposedlinkage inhowthe connection could be made between adherence and a PKPD-model.Sensitivity analysis showed that the adherence scoring used (Summary of DiabetesSelf-Care Activities measure, SDSCA) had a moderate correlation to the final response onfasting plasma glucose (Spearman ρ=−0.478∗∗∗). This result suggests that adherenceshould be considered as a relatively important factor to weave in to systems models ofpharmacology and future research should be made on further developing modelsimplementing both social factors, such as adherence, as well as pharmacologic response. Apossible way could be linking dose regimen to adherence scoring. / Farmakokinetiska (PK)- och farmakodynamiska (PD) modeller är användbara verktyg vid utvärdering av effekten av en behandlingsplan. Patientens åtlydnad tilll läkemedelsordinationen kan potentiellt vara en begränsande faktor för behandlingsprocessen. Att utveckla större system som täcker farmakologiska- samt åtlydnadsmodeller skulle potentiellt kunna vara en väg till en förhöjd förståelse angående farmakologiska system. Denna studie fokuserar på att undersöka genomförbarheten av att koppla samman modeller angående farmakokinetik, farmakodynamik samt åtlydnadsmodeller. En omfattande kartläggning av tidigare utfört arbete angående utvecklingen av PKPD-modeller och åtlydnadsmodeller som utgick fr ̊an typ 2 diabetes utfördes. Resultatet av studien visade en avsaknad av forskning gällande definieringen och kvantifieringen avhur man mäter åtlydnad för simuleringssyften. Ytterligare saknades det forskning rörande system med återkoppling från farmakologiska segment av ett system tillbaka till åtlydnadsdelarna. En enkel modell implementerades som ett förslag till hur en potentiell sammankoppling skulle kunna utföras. En känslighetsanalys utfördes och visade att poängskattningen för åtlydnad, SDSCA (Summary of Diabetes Self-Care Activities), hade en måttlig korrelation (Spearman ρ=−0.478∗∗∗) till den slutgiltiga koncentrationen av glukos i plasma. Detta resultat innebär att åtlydnad har en koppling till förbättrandet av hyperglykemi och bör därför inte exkluderas vid framtida utveckling av modeller för farmakologi. En länk skulle kunna vara kopplingen mellan ”åtlydnads-poäng” och doseringsregim.

Current excessive use and abuse of antibiotics has resulted in increasing bacterial resistance to common treatment options which is threatening to deprive us of a pillar of modern medicine. In this work methods to optimize the use of existing antibiotics and to help development of new antibiotics were developed and applied. Semi-mechanistic pharmacokinetic-pharmacodynamic (PKPD) models were developed to describe the time course of the dynamic effect and interaction of combinations of antibiotics. The models were applied to illustrate that colistin combined with a high dose of meropenem may overcome meropenem-resistant P. aeruginosa infections. The results from an in vivo dose finding study of meropenem was successfully predicted by the meropenem PKPD model in combination with a murine PK model, which supports model based dosage selection. However, the traditional PK/PD index based dose selection was predicted to have poor extrapolation properties from pre-clinical to clinical settings, and across patient populations. The precision of the model parameters, and hence the model predictions, is dependent on the experimental design. A limited study design is dictated by cost and, for in vivo studies, ethical reasons. In this work optimal design (OD) was demonstrated to be able to reduce the experimental effort in time-kill curve experiments and was utilized to suggest the experimental design for identification and estimation of an interaction between antibiotics. OD methods to handle inter occasion variability (IOV) in optimization of individual PK parameter estimates were proposed. The strategy was applied in the design of a sparse sampling schedule that aim to estimate individual exposures of colistin in a multi-centre clinical study. Plasma concentration samples from the first 100 patients have been analysed and indicate that the performance of the design is close to the predicted. The methods described in this thesis holds promise to facilitate the development of new antibiotics and to improve the use of existing antibiotics.

Background: The sensitivity to drug-induced QT prolongation is highly variable in heart failure (HF) patients. QT interval prolongation can lead to a life-threatening ventricular arrhythmia known as torsade de Pointes (TdP), which can result in sudden cardiac death. Although QT prolongation is a surrogate marker for sudden cardiac death, the extent of drug-induced QT prolongation, and thus TdP, is largely unpredictable. Therefore, developing a biomarker to predict patients’ sensitivity to drug-induced QTc prolongation could have a profound clinical impact. MicroRNA (miR) are recognized as important regulators of cardiovascular function as they shape the transcriptome by targeting mRNAs for repression of translation. Our multidisciplinary research group has demonstrated that miR-362-3p regulates a potassium channel (i.e., hERG) that is the most widely implicated in drug-induced QTc prolongation. The primary objects of this analysis focus on characterizing serum miR-362-3p expression in the circulation as a potential biomarker to predict subject’s susceptibility to ibutilide exposure induced QT-interval prolongation.<div><br></div><div>Methods: The dataset utilized to develop the PK-PD models were collected from a previous clinical study carried out by Tisdale et al. (Tisdale,et al. 2020).A total of 22 adult subjects who met the inclusion and exclusion criteria were enrolled and divided into three groups: a group of patients with heart failure with preserved ejection fraction (HFpEF, n=10), a group of patients with heart failure with reduced ejection fraction (HFrEF, n=2), and ten healthy subjects in the control group who were matched to subjects in the HFpEF group for age and sex. Following a baseline day of triplicate 12-lead ECGs, all subjects received ibutilide 0.003mg/kg intravenously infused over 10 minutes. Serial collection of blood samples to determine serum Ibutilide concentrations (HPLC/MS), serum miR-362-3 expression (qPCR), with triplicate ECG readings were obtained pre-and-post ibutilide administration. To describe ibutilide serum concentration exposure and the9relationship with Fridericia-corrected QT (QTF) intervals, a non-linear mixed effect modeling approach was used along with clinical and demographic data, and serum miR-362-3p expression was evaluated as potential covariates on the PK/PD model.<div><br></div><div>Results: A three-compartment model best described the time course of ibutilide concentrations profile with a proportional residual error. The individual ibutilide concentrations time profile was then used in an indirect response model where ibutilide concentrations are indirectly driving the QT interval prolongation through inhibition of the output (Kout) parameters linked to an indirect response model with zero‐order input parameter best described the ibutilide concentrations QT interval lengthening relationship. The Individual PK/PD parameters using the base model for the Imax and IC50 were 11.4% (9.9%RES) and 0.36(8.4% RES)ng/mL, respectively. Following stepwise forwarding inclusion steps, the final covariate analyses identified circulating miR-362-3p expression associated with a history of myocardial infarction covariate influencing both the Imax and IC50( p<0.05). <div><br></div><div>Conclusions: An indirect response model has been developed to describe the effects of ibutilide concentrations on QT-intervals. Although the semi-mechanistic model could not be developed; serummiR-362-3p expression was identified as a significant predictor for ibutilide-induced QT-interval prolongation. Moreover, the upregulation of serum miR-362-3p expression enhanced IC50 seen after ibutilide administration. The potential use of miR-362-3p as a biomarker warrants further investigation to identify patients at the greatest risk of TdP </div></div></div>

With approximately nine million new cases and the attributable cause of death of an estimated two millions people every year there is an urgent need for new and effective drugs and treatment regimens targeting tuberculosis. The tuberculosis drug development pathway is however not ideal, containing non-predictive model systems and unanswered questions that may increase the risk of failure during late-phase drug development. The aim of this thesis was hence to develop pharmacometric tools in order to optimize the development of new anti-tuberculosis drugs and treatment regimens. The General Pulmonary Distribution model was developed allowing for prediction of both rate and extent of distribution from plasma to pulmonary tissue. A distribution characterization that is of high importance as most current used anti-tuberculosis drugs were introduced into clinical use without considering the pharmacokinetic properties influencing drug distribution to the site of action. The developed optimized bronchoalveolar lavage sampling design provides a simplistic but informative approach to gathering of the data needed to allow for a model based characterization of both rate and extent of pulmonary distribution using as little as one sample per subject. The developed Multistate Tuberculosis Pharmacometric model provides predictions over time for a fast-, slow- and non-multiplying bacterial state with and without drug effect. The Multistate Tuberculosis Pharmacometric model was further used to quantify the in vitro growth of different strains of Mycobacterium tuberculosis and the exposure-response relationships of three first line anti-tuberculosis drugs. The General Pharmacodynamic Interaction model was successfully used to characterize the pharmacodynamic interactions of three first line anti-tuberculosis drugs, showing the possibility of distinguishing drug A’s interaction with drug B from drug B’s interaction with drug A. The successful separation of all three drugs effect on each other is a necessity for future work focusing on optimizing the selection of anti-tuberculosis combination regimens. With a focus on pharmacokinetics and pharmacodynamics, the work included in this thesis provides multiple new methods and approaches that individually, but maybe more important the combination of, has the potential to inform development of new but also to provide additional information of the existing anti-tuberculosis drugs and drug regimen.