20 research outputs found
Pharmacogenomics
This Special Issue focuses on the current state of pharmacogenomics (PGx) and the extensive translational process, including the identification of functionally important PGx variation; the characterization of PGx haplotypes and metabolizer statuses, their clinical interpretation, clinical decision support, and the incorporation of PGx into clinical care
Emerging technologies and their impact on regulatory science
There is an evolution and increasing need for the utilization of emerging cellular, molecular and in silico technologies and novel approaches for safety assessment of food, drugs, and personal care products. Convergence of these emerging technologies is also enabling rapid advances and approaches that may impact regulatory decisions and approvals. Although the development of emerging technologies may allow rapid advances in regulatory decision making, there is concern that these new technologies have not been thoroughly evaluated to determine if they are ready for regulatory application, singularly or in combinations. The magnitude of these combined technical advances may outpace the ability to assess fit for purpose and to allow routine application of these new methods for regulatory purposes. There is a need to develop strategies to evaluate the new technologies to determine which ones are ready for regulatory use. The opportunity to apply these potentially faster, more accurate, and cost-effective approaches remains an important goal to facilitate their incorporation into regulatory use. However, without a clear strategy to evaluate emerging technologies rapidly and appropriately, the value of these efforts may go unrecognized or may take longer. It is important for the regulatory science field to keep up with the research in these technically advanced areas and to understand the science behind these new approaches. The regulatory field must understand the critical quality attributes of these novel approaches and learn from each other's experience so that workforces can be trained to prepare for emerging global regulatory challenges. Moreover, it is essential that the regulatory community must work with the technology developers to harness collective capabilities towards developing a strategy for evaluation of these new and novel assessment tools
Model Informed Drug Development and Precision Dosing for Drug-Drug-Gene-Interactions: Application of Physiologically-Based Pharmacokinetic Modeling
The global demand for pharmaceuticals is continuously growing. As a result, one can observe an increase in adverse drug reactions, which pose a critical risk to patients. The primary triggers for adverse drug reactions are drug-drug- and drug-gene-interactions. Model-informed drug discovery and development as well as model-informed precision dosing can help to mitigate the risks of drug-drug and drug-gene interactions. Thus, this work aimed to improve and to apply physiologically-based pharmacokinetic modeling strategies in the context of model-informed drug discovery and development as well as model-informed precision dosing. For this purpose, best practices for data digitization as an essential step in the development process of most physiologically-based pharmacokinetic models have been established. Moreover, models for zoptarelin doxorubicin and simvastatin were developed and evaluated. The zoptarelin doxorubicin model was used to guide the development process of this drug. In contrast, the simvastatin model was utilized in a drug-drug-gene interaction network to generate 10368 dose recommendations for different interaction scenarios, which were made available in a digital decision support system. In conclusion, the work can be seen as a beacon project to illustrate how physiologically-based pharmacokinetic modeling of drug-drug and drug-gene interactions can be applied in model-informed drug discovery and development as well as in model-informed precision dosing.Der globale Arzneimittelbedarf steigt kontinuierlich an. Infolgedessen kommt es vermehrt zu unerwünschten Arzneimittelwirkungen, die eine Gefahr für Patienten darstellen. Eine wichtige Rolle beim Auftreten unerwünschter Arzneimittelwirkungen spielen Arzneimittel-Arzneimittel- und Arzneimittel-Gen-Wechselwirkungen. Um das Risiko solcher Wechselwirkungen zu minimieren, kann die modellgestützte Arzneimittelentwicklung und Präzisionsdosierung angewendet werden. Das Ziel dieser Arbeit war es, physiologie-basierte pharmakokinetische Modelle zum Zweck der modellgestützten Arzneimittelentwicklung und Präzisionsdosierung einzusetzen. Dafür wurde die Datendigitalisierung als wesentlicher Bestandteil der Entwicklung neuer physiologie-basierter pharmakokinetischer Modelle untersucht. Außerdem wurden Modelle für Zoptarelin Doxorubicin und Simvastatin entwickelt. Das Zoptarelin Doxorubicin Modell wurde verwendet, um die Entwicklung dieses Medikaments zu unterstützen. Mittels des Simvastatin Modells wurden in einem Interaktionsnetzwerk 10368 Dosisempfehlungen für verschiedene Szenarien generiert und in einem digitalen Entscheidungsunterstützungssystem verfügbar gemacht. Zusammenfassend kann die Arbeit als Leuchtturmprojekt gesehen werden, das zeigt, wie die physiologie-basierte pharmakokinetische Modellierung von Arzneimittel-Arzneimittel- und Arzneimittel-Gen-Wechselwirkungen in der modellgestützte Arzneimittelentwicklung und Präzisionsdosierung angewendet werden kann
Suggesting valid pharmacogenes by mining linked data
Abstract. A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, those have used only molecular networks or the biomedical literature. Here we propose a novel method that consumes an eclectic set of linked data sources to help validating uncertain drug-gene relationships. One of the advantages relies on that linked data are implemented in a standard framework that facilitates the joint use of various sources, making easy the consideration of features of various origins. Consequently, we propose an initial selection of linked data sources relevant to pharmacogenomics. We formatted these data to train a random forest algorithm, producing a model that enables classifying drug-gene pairs as related or not, thus confirming the validity of candidate pharmacogenes. Our model achieve the performance of F-measure=0.92, on a 100 folds cross-validation. A list of top candidates is provided and their obtention is discussed
Frameshift mutations at the C-terminus of HIST1H1E result in a specific DNA hypomethylation signature
BACKGROUND: We previously associated HIST1H1E mutations causing Rahman syndrome with a specific genome-wide methylation pattern. RESULTS: Methylome analysis from peripheral blood samples of six affected subjects led us to identify a specific hypomethylated profile. This "episignature" was enriched for genes involved in neuronal system development and function. A computational classifier yielded full sensitivity and specificity in detecting subjects with Rahman syndrome. Applying this model to a cohort of undiagnosed probands allowed us to reach diagnosis in one subject. CONCLUSIONS: We demonstrate an epigenetic signature in subjects with Rahman syndrome that can be used to reach molecular diagnosis
Towards Personalized Medicine: Computational Approaches to Support Drug Design and Clinical Decision Making
The future looks bright for a clinical practice that tailors the
therapy with the best efficacy and highest safety to a patient. Substantial
amounts of funding have resulted in technological advances regarding
patient-centered data acquisition --- particularly genetic data. Yet, the
challenge of translating this data into clinical practice remains open.
To support drug target characterization, we developed a global maximum
entropy-based method that predicts protein-protein complexes including the
three-dimensional structure of their interface from sequence data. To further
speed up the drug development process, we present methods to reposition drugs
with established safety profiles to new indications leveraging paths in
cellular interaction networks. We validated both methods on known data,
demonstrating their ability to recapitulate known protein complexes and
drug-indication pairs, respectively.
After studying the extent and characteristics of genetic variation with a
predicted impact on protein function across 60,607 individuals, we showed that
most patients carry variants in drug-related genes. However, for the majority
of variants, their impact on drug efficacy remains unknown. To inform
personalized treatment decisions, it is thus crucial to first collate knowledge
from open data sources about known variant effects and to then close the
knowledge gaps for variants whose effect on drug binding is still not
characterized. Here, we built an automated annotation pipeline for
patient-specific variants whose value we illustrate for a set of patients with
hepatocellular carcinoma. We further developed a molecular modeling protocol to
predict changes in binding affinity in proteins with genetic variants which we
evaluated for several clinically relevant protein kinases.
Overall, we expect that each presented method has the potential to advance
personalized medicine by closing knowledge gaps about protein interactions and
genetic variation in drug-related genes. To reach clinical applicability,
challenges with data availability need to be overcome and prediction
performance should be validated experimentally.Therapien mit der besten Wirksamkeit und höchsten
Sicherheit werden in Zukunft auf den Patienten zugeschnitten werden. Hier haben
erhebliche finanzielle Mittel zu technologischen Fortschritten bei der
patientenzentrierten Datenerfassung geführt, aber diese Daten in die
klinische Praxis zu übertragen, bleibt aktuell noch eine Herausforderung.
Um die Wirkstoffforschung in der Charakterisierung therapeutischer Zielproteine
zu unterstützen, haben wir eine Maximum-Entropie-Methode entwickelt,
die Protein-Interaktionen und ihre dreidimensionalen Struktur
aus Sequenzdaten vorhersagt. Darüber hinaus, stellen wir Methoden
zur Repositionierung von etablierten Arzneimitteln auf
neue Indikationen vor, die Pfade in zellulären Interaktionsnetze nutzen.
Diese Methoden haben wir anhand bekannter Daten validiert und ihre Fähigkeit
demonstriert, bekannte Proteinkomplexe bzw. Wirkstoff-Indikations-Paare zu
rekapitulieren.
Unsere Analyse genetischer Variation mit einem Einfluss auf die
Proteinfunktion in 60,607 Individuen konnte zeigen, dass nahezu jeder Patient
funktionsverändernde Varianten in Medikamenten-assoziierten Genen
trägt. Der direkte Einfluss der meisten beobachteten Varianten auf die
Medikamenten-Wirksamkeit ist jedoch noch unbekannt. Um dennoch personalisierte
Behandlungsentscheidungen treffen zu können, präsentieren wir eine Annotationspipeline für genetische
Varianten, deren Wert wir für Patienten mit hepatozellulärem
Karzinom illustrieren konnten. Darüber hinaus haben wir ein molekulares
Modellierungsprotokoll entwickelt, um die Veränderungen in der
Bindungsaffinität von Proteinen mit genetischen Varianten voraussagen.
Insgesamt sind wir davon überzeugt, dass jede der vorgestellten Methoden das
Potential hat, Wissenslücken über Proteininteraktionen und
genetische Variationen in medikamentenbezogenen Genen zu schlie{\ss}en und
somit das Feld der personalisierten Medizin voranzubringen. Um klinische
Anwendbarkeit zu erreichen, gilt es in der Zukunft, verbleibende
Herausforderungen bei der Datenverfügbarkeit zu bewältigen und unsere
Vorhersagen experimentell zu validieren
Phenome wide association study of vitamin D genetic variants in the UK Biobank cohort
Introduction
Vitamin D status is an important public health issue due to the high prevalence of
vitamin D insufficiency and deficiency, especially in high latitude areas. Furthermore,
it has been reported to be associated with a number of diseases. In a previous umbrella
review of meta-analyses of randomized clinical trials (RCTs) and of observational
studies, it was found that plasma/ serum 25-hydroxyvitamin D (25(OH)D) or
supplemental vitamin D has been linked to more than 130 unique health outcomes.
However, the majority of the studies yielded conflicting results and no association was
convincing.
Aim and Objectives
The aim of my PhD was to comprehensively explore the association between vitamin
D and multiple outcomes. The specific objectives were to: 1) update the umbrella
review of meta-analysis of observational studies or randomized controlled trials on
associations between vitamin D and health outcomes published between 2014 and
2018; 2) conduct a systematic literature review of previous Mendelian Randomization
studies on causal associations between vitamin D and all outcomes; 3) conduct a
systematic literature review of published phenome wide association studies,
summarizing the methods, results and predictors; 4) create a polygenic risk score of
vitamin D related genetic variants, weighted by their effect estimates from the most
recent genome wide association study; 5) encode phenotype groups based on
electronic medical records of participants; 6) study the associations between vitamin
D related SNPs and the whole spectrum of health outcomes, defined by electronic
medical records utilising the UK Biobank study; 7) explore the causal effect of 25-
hydroxyvitamin D level on health outcomes by applying novel instrumental variable
methods.
Methods
First I updated the vitamin D umbrella review published in 2015, by summarizing the
evidence from meta-analyses of observational studies and meta-analyses of RCTs
published between 2014 and 2018. I also performed a systematic literature review of
all previous Mendelian Randomizations studies on the effect of vitamin D on all health
outcomes, as well as a systematic review of all published PheWAS studies and the
methodology they applied. Then I conducted original data analysis in a large
prospective population-based cohort, the UK Biobank, which includes more than
500,000 participants. A 25(OH)D genetic risk score (weighted sum score of 6 serum
25(OH)D-related SNPs: rs3755967, rs12785878, rs10741657, rs17216707,
rs10745742 and rs8018720, as identified by the largest genome wide association study
of 25(OH)D levels) was constructed to be used as the instrumental variable. I used a
phenotyping algorithm to code the electronic medical records (EMR) of UK Biobank
participants into 1853 distinct disease categories and I then ran the PheWAS analysis
to test the associations between the 25(OH)D genetic risk score and 950 disease
outcome groups (i.e. outcomes with more than 200 cases). For phenotypes found to
show a statistically significant association with 25(OH)D levels in the PheWAS or
phenotypes which were found to be convincing or highly suggestive in previous
studies, I developed an extended case definition by incorporating self-reported data
collected by UK Biobank baseline questionnaire and interview. The possible causal
effect of vitamin D on those outcomes was then explored by the MR two-stage method,
inverse variance weighted MR and Egger’s regression, followed by sensitivity
analyses.
Results
In the updated systematic literature review of meta-analyses of observational studies
or RCTs, only studies on new outcomes which had not been covered by the previous
umbrella review were included. A total of 95 meta-analyses met the inclusion criteria.
Among the included studies there were 66 meta-analyses of observational studies, and
29 meta-analyses of RCTs. Eighty-five new outcomes were explored by meta-analyses
of observational studies, and 59 new outcomes were covered by meta-analyses of
RCTs.
In the systematic review of published Mendelian Randomization studies on vitamin D,
a total of 29 studies were included. A causal role of 25(OH)D level was supported by
MR analysis for the following outcomes: type 2 diabetes, total adiponectin, diastolic
blood pressure, risk of hypertension, multiple sclerosis, Alzheimer’s disease, all-cause
mortality, cancer mortality, mortality excluding cancer and cardiovascular events,
ovarian cancer, HDL-cholesterol, triglycerides and cognitive functions.
For the systematic literature review of published PheWAS studies and their
methodology, a total of 45 studies were included. The processes for implementing a
PheWAS study include the following steps: sample selection, predictor selection,
phenotyping, statistical analysis and result interpretation. One of the main challenges
is the definitions of the phenotypes (i.e., the method of binning participants into
different phenotype groups). In the phenotyping step, an ICD curated phenotyping was
widely used by previous PheWAS, which I also used in my own analysis.
By applying the ICD curated phenotyping, 1853 phenotype groups were defined in the
participants I used. In PheWAS, only phenotype groups with more than 200 cases were
analysed (920 phenotypes). In the PheWAS, only associations between rs17216707
(CYP24A1) and “calculus of ureter” (beta = -0.219, se = 0.045, P = 1.14*10-6), “urinary
calculus” (beta = -0.129, se = 0.027, P = 1.31*10-6), “alveolar and parietoalveolar
pneumonopathy” (beta = 0.418, se = 0.101, P = 3.53*10-5) survived Bonferroni
correction.
Nine outcomes, including systolic blood pressure, diastolic blood pressure, body mass
index, risk of hypertension, type 2 diabetes, ischemic heart disease, depression, non-vertebral
fracture and all-cause mortality were explored in MR analyses. The MR
analysis had more than 80% power for detecting a true odds ratio of 1.2 or larger for
binary outcomes. None of explored outcomes were statistically significant. Results
from multiple MR methods and sensitivity analyses were consistent.
Discussion
Vitamin D and its association with multiple outcomes has been widely studied. More
than 230 outcomes have been linked with vitamin D by meta-analyses of observational
studies and RCTs. On the contrary, evidence from Mendelian Randomization studies
is lacking. In particular I identified only 20 existing MR studies and only 13 outcomes
were suggested to be causally related to vitamin D. In the systematic literature review
of previous PheWAS studies, I summarized the applied methods, predictors and results.
Although phenotyping based on ICD codes provided good performance and was
widely applied by previous PheWAS studies, phenotyping can be improved if lab data,
imaging data and medical notes can be incorporated. Alternative algorithms, which
takes advantage of deep learning and thus enable high precision phenotyping, needs to
be developed.
From the PheWAS analysis, the score of vitamin D related genetic variants was not
statistically significantly associated with any of the 920 phenotypes tested. In the
single variant analysis, only rs17216707 (CYP24A1) was shown to be associated with
calculus outcomes statistically significantly. Previous studies reported associations
between vitamin D and hypercalcemia, hypercalciuria, nephrolithiasis and
nephrocalcinosis, may be due to the role of vitamin D in calcium homeostasis.
In the MR analysis, I found no evidence of large to moderate (OR>1.2) causal
associations of vitamin D on a very wide range of health outcomes. These included
SBP, DBP, hypertension, T2D, IHD, BMI, depression, non-vertebral fracture and allcause
mortality which have previously been proposed to be influenced by low vitamin
D levels. Further, even larger studies, probably involving the joint analysis of data
from several large biobanks with future IVs that explain a higher proportion of the trait
variance, will be required to exclude smaller causal effects which could have public
health importance because of the high population prevalence of low vitamin D levels
in some populations