| 1. |
- Karlsson, Kristin E, 1975-
(författare)
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Benefits of Pharmacometric Model-Based Design and Analysis of Clinical Trials
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Quantitative pharmacokinetic-pharmacodynamic and disease progression models are the core of the science of pharmacometrics which has been identified as one of the strategies that can make drug development more effective. To adequately develop and utilize these models one needs to carefully consider the nature of the data, choice of appropriate estimation methods, model evaluation strategies, and, most importantly, the intended use of the model. The general aim of this thesis was to investigate how the use of pharmacometric models can improve the design and analysis of clinical trials within drug development. The development of pharmacometric models for clinical assessment scales in stroke and graded severity events, in this thesis, show the benefit of describing data as close to its true nature as possible, as it increases the predictive abilities and allows for mechanistic interpretations of the models. Performance of three estimation methods implemented in the mixed-effects modeling software NONMEM; 1) Laplace, 2) SAEM, and 3) Importance sampling, applied when modeling repeated time-to-event data, was investigated. The two latter methods are to be preferred if less than approximately half of the individuals experience events. In addition, predictive performance of two validation procedures, internal and external validation, was explored, with internal validation being preferred in most cases. Model-based analysis was compared to conventional methods by the use of clinical trial simulations and the power to detect a drug effect was improved with a pharmacometric design and analysis. Throughout this thesis several examples have shown the possibility of significantly reducing sample sizes in clinical trials with a pharmacometric model-based analysis. This approach will reduce time and costs spent in the development of new drug therapies, but foremost reduce the number of healthy volunteers and patients exposed to experimental drugs.
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| 2. |
- Karlsson, Kristin E, et al.
(författare)
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Comparisons of Analysis Methods for Proof-of-Concept Trials
- 2013
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Ingår i: CPT. - : Wiley. - 2163-8306. ; 2, s. e23-
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Tidskriftsartikel (refereegranskat)abstract
- Drug development struggles with high costs and time consuming processes. Hence, a need for new strategies has been accentuated by many stakeholders in drug development. This study proposes the use of pharmacometric models to rationalize drug development. Two simulated examples, within the therapeutic areas of acute stroke and type 2 diabetes, are utilized to compare a pharmacometric model–based analysis to a t-test with respect to study power of proof-of-concept (POC) trials. In all investigated examples and scenarios, the conventional statistical analysis resulted in several fold larger study sizes to achieve 80% power. For a scenario with a parallel design of one placebo group and one active dose arm, the difference between the conventional and pharmacometric approach was 4.3- and 8.4-fold, for the stroke and diabetes example, respectively. Although the model-based power depend on the model assumptions, in these scenarios, the pharmacometric model–based approach was demonstrated to permit drastic streamlining of POC trials.
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| 3. |
- Karlsson, Kristin E., et al.
(författare)
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Modeling Disease Progression in Acute Stroke Using Clinical Assessment Scales
- 2010
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 12:4, s. 683-691
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Tidskriftsartikel (refereegranskat)abstract
- This article demonstrates techniques for describing and predicting disease progression in acute stroke by modeling scores measured using clinical assessment scales, accommodating dropout as an additional source of information. Scores assessed using the National Institutes of Health Stroke Scale and the Barthel Index in acute stroke patients were used to model the time course of disease progression. Simultaneous continuous and probabilistic models for describing the nature and magnitude of score changes were developed, and used to model the trajectory of disease progression using scale scores. The models described the observed data well, and exhibited good simulation properties. Applications include longitudinal analysis of stroke scale data, clinical trial simulation, and prognostic forecasting. Based upon experience in other areas, it is likely that application of this modeling methodology will enable reductions in the number of patients needed to carry out clinical studies of treatments for acute stroke.
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| 4. |
- Estrada, Karol, et al.
(författare)
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Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture.
- 2012
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Ingår i: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 44:5, s. 491-501
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Tidskriftsartikel (refereegranskat)abstract
- Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 × 10(-8)). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 × 10(-4), Bonferroni corrected), of which six reached P < 5 × 10(-8), including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.
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| 5. |
- Karlsson, Kristin E., et al.
(författare)
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Performance of three estimation methods in repeated time-to-event modeling
- 2011
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:1, s. 83-91
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Tidskriftsartikel (refereegranskat)abstract
- It is not uncommon that the outcome measurements, symptoms or side effects, of a clinical trial belong to the family of event type data, e.g., bleeding episodes or emesis events. Event data is often low in information content and the mixed-effects modeling software NONMEM has previously been shown to perform poorly with low information ordered categorical data. The aim of this investigation was to assess the performance of the Laplace method, the stochastic approximation expectation-maximization (SAEM) method, and the importance sampling method when modeling repeated time-to-event data. The Laplace method already existed, whereas the two latter methods have recently become available in NONMEM 7. A stochastic simulation and estimation study was performed to assess the performance of the three estimation methods when applied to a repeated time-to-event model with a constant hazard associated with an exponential interindividual variability. Various conditions were investigated, ranging from rare to frequent events and from low to high interindividual variability. The method performance was assessed by parameter bias and precision. Due to the lack of information content under conditions where very few events were observed, all three methods exhibit parameter bias and imprecision, however most pronounced by the Laplace method. The performance of the SAEM and importance sampling were generally higher than Laplace when the frequency of individuals with events was less than 43%, while at frequencies above that all methods were equal in performance.
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| 6. |
- Plan, Elodie L., et al.
(författare)
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Approaches to simultaneous analysis of frequency and severity of symptoms
- 2010
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Ingår i: Clinical Pharmacology and Therapeutics. - : Springer Science and Business Media LLC. - 0009-9236 .- 1532-6535. ; 88:2, s. 255-259
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Tidskriftsartikel (refereegranskat)abstract
- Mechanistic models that synthesize pharmacological and (patho) physiological process information provide a rich basis for the characterization of drug action. However, the underlying clinical data are often simplified in a manner that does not allow models to fully elucidate the structure of the drug effect. In this article, we describe data-simplification strategies that are in routine use to describe disease symptoms and compare them with a model developed for handling the true complexities of the data.
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| 7. |
- Coviello, Andrea D, et al.
(författare)
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A genome-wide association meta-analysis of circulating sex hormone-binding globulin reveals multiple Loci implicated in sex steroid hormone regulation.
- 2012
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Ingår i: PLoS genetics. - : Public Library of Science (PLoS). - 1553-7404 .- 1553-7390. ; 8:7
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Tidskriftsartikel (refereegranskat)abstract
- Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p=1.8×10(-106)), PRMT6 (rs17496332, 1p13.3, p=1.4×10(-11)), GCKR (rs780093, 2p23.3, p=2.2×10(-16)), ZBTB10 (rs440837, 8q21.13, p=3.4×10(-09)), JMJD1C (rs7910927, 10q21.3, p=6.1×10(-35)), SLCO1B1 (rs4149056, 12p12.1, p=1.9×10(-08)), NR2F2 (rs8023580, 15q26.2, p=8.3×10(-12)), ZNF652 (rs2411984, 17q21.32, p=3.5×10(-14)), TDGF3 (rs1573036, Xq22.3, p=4.1×10(-14)), LHCGR (rs10454142, 2p16.3, p=1.3×10(-07)), BAIAP2L1 (rs3779195, 7q21.3, p=2.7×10(-08)), and UGT2B15 (rs293428, 4q13.2, p=5.5×10(-06)). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p=2.5×10(-08), women p=0.66, heterogeneity p=0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained ∼15.6% and ∼8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance.
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