Abstract 3904: Pharmacokinetic modeling of serum platinum reveals extent of long-term exposure and associated comorbidities after cisplatin treatment
Bibliographic record
Abstract
Abstract Platinum is detectable in the plasma years after completion of cisplatin treatment. Although it has been hypothesized that circulating platinum contributes to the severity and persistence of cisplatin’s adverse effects, results have been inconsistent due to the lack of an effective pharmacokinetic model that takes into account time since therapy and the use of relatively small patient cohorts. Here, we report the largest pharmacokinetic study to date of 1,013 testicular cancer survivors (TCS) assessed 1-35 years after completion of cisplatin-based chemotherapy, and perform a genome-wide association study (GWAS) to assess genetic contributions to our pharmacokinetic phenotype computed from serum platinum levels. Eligible TCS given 300 or 400 (± 15 mg/m2) cisplatin underwent extensive audiometric testing and clinical examination, as well as completed questionnaires at the time of follow-up. We then built an empirical pharmacokinetic model by considering time since treatment and cumulative cisplatin dose received, fitting the data to a power (log-log linear) model as a log-log transformation linearized relationship with time. This power model was used to generate a residual platinum value (RPtV) that likely correlates with the area under concentration-time curve. Using linear regression, we found positive associations between RPtV and several clinically relevant phenotypes including the cumulative burden of morbidity for cisplatin-induced toxicities (β = 0.08, p = 8.42x10-3), peripheral sensory neuropathy (β = 0.14, p = 5.45x10-3), Raynaud’s phenomenon (β = 0.09, p = 0.04), hypercholesterolemia (β = 0.22, p = 0.02), LDL-cholesterol (β = 2.67x10-3, p = 0.01), luteinizing hormone (β = 0.02, p = 0.01), and age at clinical examination (β = 0.02, p = 2.04x10-7). Multinomial regression indicated that these associations are much more prominent in patients designated with high RPtV (> 1 standard deviation above the mean). In addition, we found a strong negative association with creatinine clearance (β = -9.04x10-3, p = 1.03 x10-10). GWAS implicated two SNPs that met genome-wide significance: rs78225733 (p = 6.9 x 10-9) in a gene desert on chromosome 12 and rs45623132 (p = 1.1 x 10-8), which is intronic to meningioma 1 (MN1). Taken together, our novel pharmacokinetic model indicates serum platinum levels are associated with several cisplatin-induced toxicities and comorbidities, and that finding methods by which to reduce circulating platinum once patients have been cured of their disease could be an effective strategy to improve their overall quality of life. Citation Format: Matthew R. Trendowski, Omar El-Charif, Zepeng Mu, Mark J. Ratain, Heather Wheeler, Paul C. Dinh, Darren R. Feldman, Shirin Ardeshir-Rouhani-Fard, Robert J. Hamilton, David J. Vaughn, Chunkit Fung, Taisei Mushiroda, Lawrence H. Einhorn, Lois B. Travis, M. Eileen Dolan. Pharmacokinetic modeling of serum platinum reveals extent of long-term exposure and associated comorbidities after cisplatin treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3904.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".