Population PK models aim to describe the change in drug concentration over time for a specific population. The populations in population PK modelling often refer to subjects in a clinical trial of a potential drug candidate. Population PK models are frequently described by non-linear mixed effect (NLME) models, that including both random and fixed effect components. The fixed effect components 𝜽 (THETA) portray typical parameter values in the population while the random effects components 𝜼 (ETA) allow for the incorporation of inter-individual variability (IIV) on the typical population value. The IIVs are therefore an important element of NLME models, but the estimation of the IIVs can be time consuming and become a limiting factor for more complex models. Linear approximation of the IIV’s has been suggested as a way to reduce the estimation time whilst maintaining robustness. The aim of this project was to evaluate and compare the estimation time and robustness of the IIVs for the linear approximation of parameter estimation errors in NLME models compared to those estimated in non-linear models. Population PK NLME models were developed for two datasets of phenobarbital and moxonidine. The datasets contained different levels of complexity such as number of subjects, datapoints and route of administration. The models were developed within R-studio using the assembler and Pharmpy packages and evaluated in NONMEM 7.5. Based on the objective function values (OFVs), obtained in the model building processes, selected models were linearised using Pearl speaks NONMEM (PsN). The estimated 𝜀′𝑠 and run-time of the linearised models were compared to their non-linearized counterparts. For all the models a reduction in run-time could be observed but with a slight variation in the estimations between the linearised and non-linearised models. The biggest run time reduction was seen in the oral transit compartment models for moxonidine with a 3100-fold reduction in estimation time. The estimation time reduction displayed could more quickly provide valuable information regarding the chosen error models of more complex models and while parameters estimated may not be identical to the non-linearised models, they should be sufficient during the model building phase.

<div><b>Background:</b> Bupropion, an atypical antidepressant and smoking cessation aid, is associated with wide inter-subject variability in its efficacy and safety. Variability in response to bupropion therapy is thought to be driven by variability in metabolism. Bupropion undergoes complex phase 1 and 2 stereoselective metabolism. Though bupropion`s pharmacology is not fully understood, much of it is thought to be due to its metabolites, specially, S, S-hydroxybupropion. In vitro studies (functional assays measuring IC50 at dopamine transporter-DAT, norepinephrine transporter-NET, various subtypes of nicotinic receptors-nAChR) and mouse models (forced swim test to assess antidepressant effect, antinociceptive models to assess antagonism of nicotine effects) indicate S, S-hydroxybupropion to contribute more towards efficacy as an antidepressant and smoking cessation aid than racemic bupropion and R, R-hydroxybupropion, respectively. Both pharmacokinetics (PK) and pharmacodynamics (PD) of bupropion and its metabolites are complex and reported to be stereoselective. As bupropion is known to act on multiple central nervous system (CNS) targets (DAT, NET nAChR), understanding CNS disposition (target site) is critical to explain variability in bupropion`s therapeutic and toxic effects. </div><div><b>Objective: </b>The objective of our study was to characterize the exposure of bupropion enantiomers and corresponding phase 1 metabolite diastereomers in plasma and brain in a surrogate non-clinical species, and to subsequently develop animal-to-human-translational population-PK and Physiologically Based PK (PBPK) models to predict human brain concentrations of bupropion and its active metabolite S, S-hydroxybupropion. Application of these PK modeling approaches to map the time course of unbound brain concentration can then be compared to in vitro potency measures at DAT, NET and nAChRs to predict target engagement over time (PD). Establishing relationships between plasma PK, target site PK along with PD would elucidate possible cause(s) of inter-patient variability to bupropion therapy. </div><div><b>Methods: </b>The first step towards development of a CNS model was to identify a nonclinical species with phase 1 metabolism closest to humans. To accomplish this, hepatic microsomal incubations of four species-rat, mouse, non-human primates (NHPs) and humans were conducted separately for the R- and S-bupropion enantiomers, and the formation of enantiomer-specific metabolites was determined using LC-MS/MS. Intrinsic formation clearance (CLint) of metabolites across the four species (rats, mice, NHPs, humans) was determined from the formation rate versus substrate concentration relationship. </div><div>Racemic bupropion (10 mg/kg) and preformed S, S-hydroxybupropion (2 mg/kg) were administered subcutaneously to adult male Sprague Dawley rats (n = 24/compound). Brain and plasma were collected from rats (n = 3) at eight time points for 6 hours and analyzed using a chiral LC-MS/MS method. Rat plasma protein and brain homogenate binding studies were conducted for all analytes to correct for unbound fraction using equilibrium dialysis method.</div><div>A plasma-brain compartmental pharmacokinetic approach was used to describe the blood–brain-barrier transport of both bupropion and S, S-hydroxybupropion. Also, a 2-compartment permeability-limited brain model consisting of brain blood, brain mass compartments was developed and incorporated into a whole body physiologically-based pharmacokinetic (PBPK) parent-metabolite model for bupropion and S, S-hydroxybupropion. Both population PK and PBPK modeling approaches were subsequently translated to humans to predict human plasma and brain site exposure and its relationship to DAT and NET IC50 potencies.</div><div><b>Results: </b>The total clearance of S-bupropion was higher than that of R-bupropion in monkey and human liver microsomes. The contribution of hydroxybupropion to the total racemic bupropion clearance was 38%, 62%, 17%, and 96% in human, monkey, rat, and mouse, respectively. In the same species order, threohydrobupropion contributed 53%, 23%, 17%, and 3%, and erythrohydrobupropion contributed 9%, 14%, 66%, and 1.3%, respectively, to racemic bupropion clearance. Hepatic microsomal incubation studies indicated non-human primates to be the appropriate species to model CNS disposition. However, the cost and limited pharmacokinetic and pharmacodynamic data in NHPs were insurmountable barriers to conducting in vivo studies in NHPs. After considering multiple factors, such as the formation of reductive metabolites (higher in rats than mice), which are also thought to contribute to bupropion`s therapeutic efficacy, availability of microdialysis data measuring bupropion and dopamine, norepinephrine levels in brain extracellular fluid (ECF) and other in vitro potency evaluations in rats, rat was chosen as the surrogate species to model bupropion`s disposition.</div><div>In rats, unbound plasma and brain exposures and plasma clearances of both R and S-bupropion were similar. The exposure to parent was higher (50 to 100-fold) than to metabolites. The exposure of oxidative metabolites (R, R- and S, S-hydroxybupropion) was 2 to 3-fold higher in brain and plasma than reductive metabolites (R, R- and S, S-threohydrobupropion, S, R- and R, S-erythrohydrobupropion). Hepatic clearances of R- and S-bupropion scaled from in vitro rat hepatic microsomal incubation studies were 3-fold and 25-fold lower than their respective in vivo unbound apparent clearances. This could possibly be due to substantial contribution of metabolic pathways not characterized in this in vivo study and/or possible extrahepatic disposition in the rat. The unbound brain to unbound plasma AUC0-6h ratio (Kp,uu) of R- and S-bupropion were 0.43 and 0.38 respectively. Kp,uu of oxidative metabolites (R, R- and S, S-hydroxybupropion) and reductive metabolites (R, R- and S, S-threohydrobupropion) were close to 1. Kp,uu of S, R-erythrohydrobupropion was 0.43 and that of pre-formed S, S-hydroxybupropion was 5.</div><div>With respect to population PK modeling of both bupropion and S, S-hydroxybupropion, a plasma-brain compartmental model structure with time dependent change in brain influx clearance was required to adequately characterize the BBB transport of parent and this active metabolite. Using a physiologically-based pharmacokinetic model (PBPK) approach too, incorporation of active efflux and carrier mediated uptake terms in addition to passive permeability was necessary to adequately characterize brain disposition of bupropion and S, S-hydroxybupropion. Both modeling approaches (population-PK and PBPK) when translated to humans indicated that the predicted human brain exposures fall below the reported DAT and NET IC50 measures of bupropion and S, S-hydroxybupropion. </div><div><b>Conclusion: </b>Specific to our work in the rat, the discrepancy between in vitro scaled hepatic clearance and in vivo plasma clearance of R and S-bupropion suggests alternative non-CYP mediated clearance pathways and/or extra hepatic disposition of bupropion. Both translational PK models indicate active process such as efflux transporter or carrier mediated uptake could be involved in bupropion`s disposition in the brain. Variability in expression of these speculated active/carrier mediated transporters could possibly cause variability in response. Also, other CNS targets could contribute to bupropion`s therapeutic efficacy, elucidation of which would require further investigation.</div><div><br></div>

1.   Sammanfattning Introduktion: Det finns idag flera allvarliga infektioner som har utmanande farmakologisk behandling, där tuberkulos är en av dem. Patogenerna som orsakar tuberkulos är svåra att behandla på grund av deras resistensmekanism, vilket gör att antibiotika inte fungerar lika effektivt längre som de gjorde tidigare. Ett sätt att studera nya antibiotika är att använda hollow fiber modellen. Hollow fiber modellen är en metod som utnyttjar en kassett med semipermeabla filter som tillåter att antibiotikumet fluktuerar i systemet, vilket ska efterlikna människokroppens plasmakoncentration. Detta är ett mer anpassat system jämfört med konventionella in vitro studier som undersöker konstanta koncentrationer i plasma, för att hollow fiber modellen visar effekten vid fluktuerande koncentrationer. Syfte: Syftet med studien var att hitta tillgänglig information om hollow fiber systemet, vilka studier som har gjorts och hur man kan modifiera systemets egenskaper, som pumphastigheter och antal flaskor, för att efterlikna farmakokinetiken i kroppen på ett optimalt sätt. Metod: Studien är en litteraturöversikt som använt sig av Pubmed och Web of Science Core Collection för att samla in vetenskapliga artiklar. Ett urvalssystem har använts för att samla in artiklar som är relevanta till syftet och sökord som använts var ”hollow fiber”, ”HFS”, ”in vitro” och ”tuberculosis”. Inklusionskriterier var artiklar på engelska och maximalt fem år gamla och exklusionskriterier var studier som inte använde sig av hollow fiber systemet för att studera bakterier som patogener. Förutom insamling av data från vetenskapliga artiklar utfördes en intervju med två experimentalister inom området hollow fiber infektionsmodellen. Resultat och diskussion: Baserat på studierna används hollow fiber modellen för att undersöka 1) läkemedelskombinationer, 2) bakterieresistens och 3) hur PD beror på PK-parametrar. Parametrar som behövs vid studien är Cmax, Cmin, tmax, halveringstid, clearance och AUC (farmakokinetiska), som resulterar i TTP, CFU, MIC, EC50 och Emax (farmakodynamiska). Beroende på typ av läkemedel anpassas de olika farmakokinetiska modellerna till systemet och därför krävs ibland en kombination av pumpar och flödeshastigheter, speciellt om flera läkemedel ska testas eller ifall läkemedlet följer mer komplex farmakokinetisk distribution och elimination än one-compartment kinetik. Systemet kan även modifieras så att den kan efterlikna absorptionsfasen ifall läkemedlet ska konstrueras till en peroral administrering (tablett, kapsel, oral lösning).  Slutsats: Hollow fiber systemet har använts för att undersöka antibiotikas bakteriedödande effekt. Olika farmakokinetiska modeller kan anpassas till systemet för att efterlikna den avdödande effekten i kroppen, genom att ändra antal flaskor, pumpar och flödeshastigheterna i pumpinställningarna.

付記する学位プログラム名: 充実した健康長寿社会を築く総合医療開発リーダー育成プログラム / 京都大学 / 0048 / 新制・課程博士 / 博士(薬科学) / 甲第22396号 / 薬科博第118号 / 新制||薬科||13(附属図書館) / 京都大学大学院薬学研究科薬科学専攻 / (主査)教授 髙倉 喜信, 教授 山下 富義, 教授 小野 正博 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

Homologous recombination (HR) and nonhomologous end joining (NHEJ) are two distinct DNA double-stranded break (DSB) repair pathways. Here, we report that DNA-dependent protein kinase (DNA-PK), the core component of NHEJ, partnering with DNA-damage checkpoint kinases ataxia telangiectasia mutated (ATM) and ATM- and Rad3-related (ATR), regulates HR repair of DSBs. The regulation was accomplished through modulation of the p53 and replication protein A (RPA) interaction. We show that upon DNA damage, p53 and RPA were freed from a p53-RPA complex by simultaneous phosphorylations of RPA at the N-terminus of RPA32 subunit by DNA-PK and of p53 at Ser37 and Ser46 in a Chk1/Chk2-independent manner by ATR and ATM, respectively. Neither the phosphorylation of RPA nor of p53 alone could dissociate p53 and RPA. Furthermore, disruption of the release significantly compromised HR repair of DSBs. Our results reveal a mechanism for the crosstalk between HR repair and NHEJ through the co-regulation of p53-RPA interaction by DNA-PK, ATM and ATR.

Homologous recombination (HR) and nonhomologous end joining (NHEJ) are two distinct DNA double-stranded break (DSB) repair pathways. Here, we report that DNA-dependent protein kinase (DNA-PK), the core component of NHEJ, partnering with DNA-damage checkpoint kinases ataxia telangiectasia mutated (ATM) and ATM- and Rad3-related (ATR), regulates HR repair of DSBs. The regulation was accomplished through modulation of the p53 and replication protein A (RPA) interaction. We show that upon DNA damage, p53 and RPA were freed from a p53-RPA complex by simultaneous phosphorylations of RPA at the N-terminus of RPA32 subunit by DNA-PK and of p53 at Ser37 and Ser46 in a Chk1/Chk2-independent manner by ATR and ATM, respectively. Neither the phosphorylation of RPA nor of p53 alone could dissociate p53 and RPA. Furthermore, disruption of the release significantly compromised HR repair of DSBs. Our results reveal a mechanism for the crosstalk between HR repair and NHEJ through the co-regulation of p53-RPA interaction by DNA-PK, ATM and ATR.

This study was undertaken to investigate whether the developing brain adapts at biochemical level against neonatal hypothyroidism, as it does so against a variety of physiological disturbances. A moderate hypothyroid state in mice neonates was induced by supplementing 0.05% methimazole in drinking water to the mothers up to suckling period, and its effect on concerted development of the enzymes regulating metabolic channeling of glucose vis a vis glucose phosphorylating activity were studied. In the brain of control mice, the activity of glucose-6-phosphate dehydrogenase (G6PDH), that channels glucose in biosynthetic route (Pentose phosphate pathway, PPP), increased slightly (∼ 1.3 times) from day1 to 10w age. However, glucose phosphorylating activity and the enzymes that commit glucose for energy production, viz phosphofructokinase1 (PFK1), pyruvate kinase (PK) and lactate dehydrogenase (LDH) showed a progressive postnatal increase to attain their respective adult levels (∼ 5-10 times higher than 1day value) by 3-10w ages of mice. In comparison to the control, in the brain of age matched neonatal hypothyroid mice, glucose phosphorylating activity, G6PDH and PFK1 increased significantly (p<0.001) at day1. Thereafter, though, glucose phosphorylating activity continued to increase up to 1w age and remained static thereafter, G6PDH declined significantly (p<0.001) from 1w onward ages. On the other hand, as PFK1 activity increased significantly up to 10w age (p<0.001), the ratio of G6PDH/PFK1 showed a marked decline from 1w onward ages. PK and LDH also showed increasing trend up to 3w age in the brain of hypothyroid mice pups. The results suggest that a moderate hypothyroid state, during the period of rapid brain growth (day 1-1w age), stimulates all the enzymes that regulate channeling of glucose in both, the energy yielding and biosynthetic paths. However, the later postnatal ages, it modulates these enzymes in a metabolic path dependent manner.

A piano key (PK) weir is a type of nonlinear (labyrinth-type) weir developed specifically for free-surface flow control structures with relatively small spillway footprints. Currently, no generally accepted standard PK weir design procedure is available. This is due, in part, to the large number of geometric parameters and a limited understanding of their effects on discharge efficiency (discharge efficiency is quantified by the discharge coefficient of the standard weir equation). However, Hydrocoop, a non-profit French dam spillways association, has recommended a PK weir design and a head-discharge relationship specific to that geometry. To develop a better understanding of the effects of PK weir geometry on discharge efficiency, 13 laboratory-scale, 4-cycle PK and rectangular labyrinth weir configurations were tested. As a result, the influence of the following PK weir geometries and/or modifications on discharge efficiency were partially isolated: the inlet-to-outlet key width ratio, upstream, and downstream apex overhangs; sloped floors; raising the crest elevation via a parapet wall; fillets underneath the upstream overhangs; and the crest type. The physical model test matrix also included a PK weir configuration consistent with the Hydrocoop-recommended design. From the experimental results, the appropriateness of the Hydrocoop-recommended head-discharge relationship was evaluated, along with the discharge coefficient behavior associated with the standard weir equation. Finally, trapezoidal labyrinth weirs were compared to PK weirs to make a relative comparison of nonlinear weir discharge efficiency; comparisons were made considering crest length and structure footprint.

Nonlinear Mixed effect models are often used to describe population pharmacokinetics (PK) and Pharmacodynamics (PD) and play an important part of drug development both from regulatory and industry point of view. However, they can be time consuming and computationally expensive to develop. This thesis is a part of a larger collaboration between Uppsala University and two pharmaceutical companies, with the aim to develop a suite of software that can automate the model building process with more efficiency. One aspect that is important during the model building process is to detect how much the population parameter estimates are influenced by particular individuals. The results of this might lead to reconsideration of the model structure, as well as exclusion of these individuals from the dataset. The current tools available to detect this use case deletion diagnostics (CDD) to run the model multiple times with each subject removed from the dataset to examine whether the population estimates alter when that individual is removed. Another important aspect is whether an individual is an outlier from the population parameter predictions, which is obtained from simulating the model and evaluating the residuals (simeval). Both of these tools are computationally expensive and can take a lot of time, in particular CDD. Therefore, we developed a tool using machine learning (ML) algorithms that can predict these individuals based on other criteria, which will decrease the runtime in an automated model building procedure, whilst maintaining the robustness of the current methods described above.  To create a training database for the ML models, predictors were extracted from 27 previously published models and the CDD and simeval diagnostic tools were run on these models to obtain that true values we want the ML model to predict. The database was then used to train two artificial neural networks (ANN) which is an efficient and powerful method in ML. To enable ‘on-the-fly’ predictions, the developed ANN models were deployed using tflite into pharmpy. The resulted ANNs were able to predict outlying individuals with 79% sensitivity, 83% precision, and 99.1% specificity. While the influential individuals ANN was able to predict with 58% sensitivity, 63% precision and 99.6% specificity. Both ANNs offered a rapid assessment of influential individuals and outlying individuals and were able to make predictions in a matter of sub-seconds compared to hours using traditional methods.

No description available.