Considerations in the development of circulating tumor cell technology for clinical use.

This manuscript summarizes current thinking on the value and promise of evolving circulating tumor cell (CTC) technologies for cancer patient diagnosis, prognosis, and response to therapy, as well as accelerating oncologic drug development. Moving forward requires the application of the classic steps in biomarker development-analytical and clinical validation and clinical qualification for specific contexts of use. To that end, this review describes methods for interactive comparisons of proprietary new technologies, clinical trial designs, a clinical validation qualification strategy, and an approach for effectively carrying out this work through a public-private partnership that includes test developers, drug developers, clinical trialists, the US Food & Drug Administration (FDA) and the US National Cancer Institute (NCI).

Adaptive Bayesian analysis of serum creatinine as a marker for drug-induced renal impairment in an early-phase clinical trial.

BACKGROUND: A concern with using creatinine for the identification of drug-induced renal impairment is that small changes in serum creatinine (SCr) that frequently are perceived as measurement bias or imprecision translate into important changes in the glomerular filtration rate. Important drug-generated changes in creatinine are difficult to detect because they are frequently observed within the reference interval. The design of a crossover drug protocol is an opportunity to use study participants as their own control to identify these small but important changes. METHODS: Twenty individuals participating in a phase I clinical trial were evaluated for SCr changes beyond those expected for biological variation according to individual Z scores derived from an adaptive Bayesian model. After 2 screening tests, participants were administered either drug (n = 11) or placebo (n = 9) during the first dosing interval. A washout period followed, and drug was then administered to the group that initially received placebo, and vice versa (10 visits total per participant). RESULTS: Although all creatinine values fell within the reference interval, 8 participants individually showed increased concentrations (Z scores >2.33). These 8 participants were confirmed at unblinding to have received the drug in the identified dosing period, with 1 exception. CONCLUSIONS: The ability to identify a drug effect on an individual-participant basis in early-phase studies permits drug developers to recognize issues early in development and rapidly engage in risk-benefit analysis. These results suggest that SCr monitoring is able to detect early kidney dysfunction when individual-based reference intervals are used.

Adaptive Bayesian approach to clinical trial renal impairment biomarker signal from urea and creatinine.

A major concern with the identification of renal toxicity using the traditional biomarkers, urea and creatinine, is that toxicity signal definitions are not sensitive to medically important changes in these biomarkers. Traditional renal signal definitions for urea and creatinine have not adequately identified drugs that have generated important medical issues later in development. Here, two clinical trial databases with a posteriori known drug induced renal impairment were analyzed for the presence of a renal impairment biomarker signal from urea (590 patients; age 26-92, median 65) and creatinine (532 patients; age 26-97, median 65). Data was analyzed retrospectively using multiple definitions for the biomarker signal to include values outside stratified reference intervals, values exceeding twofold increases from baseline, values classified by the 2009 NIAID renal toxicity table, change from baseline represented as a Z-score based on intra-individual biological variations, and an adaptive Bayesian methodology that generalizes population- with individual-based methods for evaluating a biomarker signal. The data demonstrated that the adaptive Bayesian methodology generated a prominent drug induced signal for renal impairment at the first visit after drug administration. The signal was directly related to dose and time of drug administration. All other data analysis methods produced none or significantly weaker signals than the adaptive Bayesian approach. Interestingly, serum creatinine and urea are able to detect early kidney dysfunction when the biomarker signal is personalized.

Patient-specific measures of a biomarker for the generation of individual reference intervals: hemoglobin as example.

Although hemoglobin concentration measurement is among the most commonly performed blood tests, the description of global population parameters, heterogeneous factors, and within-subject variations in patients with disease remains incomplete. As absolute action values are being published in the medical literature and by government healthcare agencies, these measures are important to define patient-specific ranges of biomarkers. Here, a global clinical trial data set composed of 1,537,932 hemoglobin values from 416,374 patients and 372 clinical indications was generated over 2 years by automated analyzers in a global network of 5 laboratories. Within- and between-subject components of variance and the effect of factors age, gender, nationality, and clinical indication were determined using unbalanced multiway analysis of variance. Average within-subject variances differed significantly depending on the clinical indication (0.15-1.3 g(2)/dL(2)) but, nevertheless, remained significantly lower than between-subject variances. The main sources of between-subject variation were clinical indication and gender, followed by age and nationality. An adaptive Bayesian approach was then used to generate patient-specific ranges of hemoglobin for drug safety and efficacy assessment in clinical trials. The same methodology can be applied to the evaluation of any biomarker signal in translational medicine.

Assay validation for KIM-1: human urinary renal dysfunction biomarker.

Urinary kidney injury molecule (KIM-1) is a sensitive quantitative biomarker for early detection of kidney tubular injury. The objective of the present work was to analytically validate this urinary renal injury biomarker. Duo-set reagents from R&D were used to develop the ELISA and validate the assay's linearity, intra-run precision, inter-run precision, lower limit of quantification, recovery, dilutional verification, reference range, stability, and length of run. The reference range data suggests that the healthy population falls within the assay range (59 - 2146 pg/mL) and upper limit of quantitation for this assay is 17168 pg/mL for the patient population. This is a robust assay to detect urinary levels of KIM-1, which serves as a non-invasive sensitive, reproducible, and potentially high-throughput method to detect early kidney injury in drug development studies.