Ida Netterberg

ORCID: 0000-0003-4677-4741
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About
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Research Areas
  • Cancer Genomics and Diagnostics
  • Statistical Methods in Clinical Trials
  • Lung Cancer Treatments and Mutations
  • Radiomics and Machine Learning in Medical Imaging
  • Mathematical Biology Tumor Growth
  • Health Systems, Economic Evaluations, Quality of Life
  • Colorectal Cancer Treatments and Studies
  • Economic and Financial Impacts of Cancer
  • Drug-Induced Hepatotoxicity and Protection
  • Cancer Immunotherapy and Biomarkers
  • Neutropenia and Cancer Infections
  • Pharmacological Effects and Toxicity Studies
  • Hematological disorders and diagnostics
  • Drug Transport and Resistance Mechanisms
  • Cancer-related molecular mechanisms research
  • PARP inhibition in cancer therapy
  • Pancreatic and Hepatic Oncology Research
  • CAR-T cell therapy research
  • MicroRNA in disease regulation
  • Immunotherapy and Immune Responses
  • Computational Drug Discovery Methods
  • thermodynamics and calorimetric analyses
  • Cytokine Signaling Pathways and Interactions
  • Inflammatory Biomarkers in Disease Prognosis
  • Cancer Cells and Metastasis

Uppsala University
2017-2023

Duke University
2013

Impaired hepatic bile acid export may contribute to development of cholestatic drug-induced liver injury (DILI). The multidrug resistance-associated proteins (MRP) 3 and 4 are postulated be compensatory basolateral efflux transporters when biliary excretion by the salt pump (BSEP) is impaired. BSEP inhibition a risk factor for DILI. This study aimed characterize relationship between MRP3, MRP4, potential drugs. inhibitory effect 88 drugs (100 <i>μ</i>M) on MRP3- MRP4-mediated substrate...

10.1124/dmd.113.054304 article EN Drug Metabolism and Disposition 2013-10-23

To assess circulating biomarkers as predictors of antitumor response to atezolizumab (anti-programmed death-ligand 1 (PD-L1), Tecentriq) serum pharmacokinetic (PK) and 95 plasma were analyzed in 88 patients with relapsed/refractory non-small cell lung cancer (NSCLC) receiving i.v. q3w (10-20 mg/kg) the PCD4989g phase I clinical trial. Following exploratory analyses, two chosen for further study correlation change tumor size (the sum longest diameter) was assessed a...

10.1002/cpt.1198 article EN cc-by-nc-nd Clinical Pharmacology & Therapeutics 2018-07-30

Early identification of patients with febrile neutropenia (FN) is desirable for initiation preventive treatment, such as antibiotics. In this study, the time courses two inflammation biomarkers, interleukin (IL)-6 and C-reactive protein (CRP), following adjuvant chemotherapy breast cancer, were characterized. The potential to predict development FN by IL-6 CRP, other model-derived clinical variables, was explored.The CRP in cycles 1 4 cancer treatment described turnover models where...

10.1111/bcp.13477 article EN British Journal of Clinical Pharmacology 2017-11-27

The large heterogeneity in response to immune checkpoint inhibitors is driving the exploration of predictive biomarkers identify patients who will respond such treatment. We extended our previously suggested modeling framework atezolizumab pharmacokinetics, IL18, and tumor size (TS) dynamics, also include overall survival (OS). Baseline model‐derived variables were explored as predictors OS 88 with non‐small cell lung cancer treated atezolizumab. To investigate impact follow‐up length on...

10.1002/psp4.12489 article EN cc-by-nc CPT Pharmacometrics & Systems Pharmacology 2020-01-28

Abstract Purpose: Quantitative relationships between treatment-induced changes in tumor size and circulating cell (CTC) counts, their links to overall survival (OS), are lacking. We present a population modeling framework identifying quantifying such relationships, based on longitudinal data collected patients with metastatic colorectal cancer (mCRC) evaluate the value of CTC counts as predictors OS. Experimental Design: A pharmacometric approach (i.e., pharmacodynamic modeling) was used...

10.1158/1078-0432.ccr-19-2570 article EN Clinical Cancer Research 2020-06-11

&lt;p&gt;VPC of the tumor size model taking dropout into account. The solid and dashed lines in left panel represent observed median 5th (lower line) 95th (upper percentiles data, respectively. shaded areas are corresponding 95% CIs simulated derived from 200 simulations final model. grey horizontal line represents SLD 10 mm.&lt;/p&gt;

10.1158/1078-0432.22475183.v1 preprint EN 2023-03-31

&lt;p&gt;KMMC VPCs of the base (top plots) and final (bottom OS model. The observed mean BTS age (black lines), in comparison to 95% CI based on 100 simulations from models (shaded areas), patients remaining study over time are illustrated left right plots, respectively. Vertical lines indicate censored events.&lt;/p&gt;

10.1158/1078-0432.22475177.v1 preprint EN cc-by 2023-03-31

&lt;div&gt;AbstractPurpose:&lt;p&gt;Quantitative relationships between treatment-induced changes in tumor size and circulating cell (CTC) counts, their links to overall survival (OS), are lacking. We present a population modeling framework identifying quantifying such relationships, based on longitudinal data collected patients with metastatic colorectal cancer (mCRC) evaluate the value of CTC counts as predictors OS.&lt;/p&gt;Experimental Design:&lt;p&gt;A pharmacometric approach (i.e.,...

10.1158/1078-0432.c.6529016.v1 preprint EN 2023-03-31

&lt;div&gt;AbstractPurpose:&lt;p&gt;Quantitative relationships between treatment-induced changes in tumor size and circulating cell (CTC) counts, their links to overall survival (OS), are lacking. We present a population modeling framework identifying quantifying such relationships, based on longitudinal data collected patients with metastatic colorectal cancer (mCRC) evaluate the value of CTC counts as predictors OS.&lt;/p&gt;Experimental Design:&lt;p&gt;A pharmacometric approach (i.e.,...

10.1158/1078-0432.c.6529016 preprint EN 2023-03-31

&lt;p&gt;VPC of the proportions CTC count equal to 0, 1, 2, 3, &gt;3 and {less than or to}5, &gt;5 to}10, &gt;10 to}50, &gt;500 to}100 &gt;100 over time in final model. The dots represent observed (connected with a line) shaded areas are 95% CI simulated data, derived from 200 simulations model.&lt;/p&gt;

10.1158/1078-0432.22475180.v1 preprint EN cc-by 2023-03-31

&lt;p&gt;Schematic representation of the tumor size model. The total SLD is a sum all compartments in pink (drug sensitive fraction) and orange resistant boxes.&lt;/p&gt;

10.1158/1078-0432.22475189.v1 preprint EN cc-by 2023-03-31

&lt;p&gt;Individual tumor size predictions in the final model (lines) and observed sizes (dots) for 9 example patients (indicated by shape of dots), 3 each subpopulation.&lt;/p&gt;

10.1158/1078-0432.22475186.v1 preprint EN cc-by 2023-03-31

&lt;p&gt;VPC of the tumor size model taking dropout into account. The solid and dashed lines in left panel represent observed median 5th (lower line) 95th (upper percentiles data, respectively. shaded areas are corresponding 95% CIs simulated derived from 200 simulations final model. grey horizontal line represents SLD 10 mm.&lt;/p&gt;

10.1158/1078-0432.22475183 preprint EN cc-by 2023-03-31

&lt;p&gt;Individual tumor size predictions in the final model (lines) and observed sizes (dots) for 9 example patients (indicated by shape of dots), 3 each subpopulation.&lt;/p&gt;

10.1158/1078-0432.22475186 preprint EN cc-by 2023-03-31

&lt;p&gt;VPC of the proportions CTC count equal to 0, 1, 2, 3, &gt;3 and {less than or to}5, &gt;5 to}10, &gt;10 to}50, &gt;500 to}100 &gt;100 over time in final model. The dots represent observed (connected with a line) shaded areas are 95% CI simulated data, derived from 200 simulations model.&lt;/p&gt;

10.1158/1078-0432.22475180 preprint EN cc-by 2023-03-31

&lt;p&gt;Schematic representation of the tumor size model. The total SLD is a sum all compartments in pink (drug sensitive fraction) and orange resistant boxes.&lt;/p&gt;

10.1158/1078-0432.22475189 preprint EN cc-by 2023-03-31
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