Recently a framework was presented to assess whether pediatric covariate models for clearance can be extrapolated between drugs sharing elimination pathways, based on extraction ratio, protein binding, and other drug properties. Here we evaluate when a pediatric covariate function for midazolam clearance can be used to scale clearance of other CYP3A substrates. A population PK model including a covariate function for clearance was developed for midazolam in children aged 1–17 years. Commonly used CYP3A substrates were selected and using the framework, it was assessed whether the midazolam covariate function accurately scales their clearance. For eight substrates, reported pediatric clearance values were compared numerically and graphically with clearance values scaled using the midazolam covariate function. For sildenafil, clearance values obtained with population PK modeling based on pediatric concentration-time data were compared with those scaled with the midazolam covariate function. According to the framework, a midazolam covariate function will lead to systemically accurate clearance scaling (absolute prediction error (PE) < 30%) for CYP3A substrates binding to albumin with an extraction ratio between 0.35 and 0.65 when binding < 10% in adults, between 0.05 and 0.55 when binding > 90%, and with an extraction ratio ranging between these values when binding between 10 and 90%. Scaled clearance values for eight commonly used CYP3A substrates were reasonably accurate (PE < 50%). Scaling of sildenafil clearance was accurate (PE < 30%). We defined for which CYP3A substrates a pediatric covariate function for midazolam clearance can accurately scale plasma clearance in children. This scaling approach may be useful for CYP3A substrates with scarce or no available pediatric PK information.

MammaPrint was the first genomic assay in breast cancer to be validated with a prospective randomized trial, the MINDACT trial. The 70 gene MammaPrint assay was developed to determine the risk of distant metastasis in early stage breast cancer through gene expression analysis and was the first FDA cleared genomic assay for breast cancer. The assay identifies primary breast cancers likely to metastasize within the first five years of diagnosis and has clinical utility for helping to determine the expected benefit from adjuvant chemotherapy. The MINDACT Trial was the first trial of a genomic assay in breast cancer to provide prospective, randomized evidence of clinical utility for this important clinical question, identifying a significant proportion of patients who could safely forgo chemotherapy within a cohort of patients with high risk clinical characteristics. Nearly half of all patients (46%) who would have been advised chemotherapy according to clinical guidelines were identified genomically by MammaPrint as being low risk and found to have equivalent rates of freedom from metastasis at 5 years with or without chemotherapy. Based upon the MINDACT trial, the ASCO Biomarker Guidelines now approve the use of MammaPrint to inform decisions regarding chemotherapy for women with clinically high-risk ER+ breast cancer, and as the only approved assay for use in women with 1-3 involved lymph nodes. Recent studies suggest information obtained from the 70-gene assay may also help inform decisions regarding endocrine therapy, as well as chemotherapy, targeted therapy and immunotherapy. CONCLUSION: The power of gene expression analysis in breast cancer, effectively illustrated with MammaPrint in the MINDACT trial, is now being explored through examination of the full transcriptome in breast cancer.

OBJECTIVE This paper describes our experience and outcomes from 54 cases presented to the (Molecular tumor board) MTB. METHODS 54 Cases presented between July 2017 and April 2018 were included in this analysis. These patients had different types of cancers that had either failed standard therapy or were expected to fail and physicians were looking for future options for anticipated progression. Patients who had obvious mutations and were candidates for Targeted Agent and Profiling Utilization Registry or Molecular Analysis for Treatment Choice clinical trials were not included. Oncologists presented the cases virtually and Foundation Medicine scientific and clinical team discussed the molecular pathways to find targeted options or trials. Tumor board attendees included oncologists, nurses, pharmacists, mid-level providers, residents and staff of the Cancer Center. RESULTS Amongst the 54 cases presented 81% had one or more potentially actionable alteration. 12 (22%) patients received genomically matched therapy as per MTB recommendations. Additional 13 (24%) patients have options available when they progress. Out of 12 patients who got treatment six are alive at the time of this analysis. Genomically matched therapy or Clinical Trials option were offered to the 46% of patients based on the MTB discussion. CONCLUSION More widespread use of molecular diagnostics, better physician education and multidisciplinary collaboration between the staff involved in diagnosis and treatment, as well as third party payers are necessary for consensus on treatment and care of oncology patients.

The current paper discusses the use of genomics in the context of the changing landscape of clinical practice and modern medicine. Medical practice has shifted considerably over the past few decades, from empirical to evidence-based to personalized medicine, and the transition from reliance on observation to measureable parameters. Scientific innovation is required to collect an ever-increasing number and variety of data points and sophisticated analyses capable of distilling vast datasets into meaningful information. The next phase of innovation seeks to personalize disease management, in particular through genomics in oncology. With expanding use of genomics in medicine, and several initiatives collecting genomic data at the population level, education of patients and physicians is critical for data utility. By combining genomic and clinical data, bioinformatics approaches can be applied to developing individualized or targeted therapies. Breast cancer provides an example through which to understand the evolution of genomic data from pure science to clinical utility. From intrinsic subtype classification to development of multigene panels estimating recurrence risk, new studies, such as the FLEX trial, will expand to evaluate the whole transcriptome of tumours. This approach will enable discovery of novel gene signatures and ultimately pave the way toward a personalized approach to breast cancer management. CONCLUSION: Despite the potential for genomics to personalize treatments, a number of challenges remain to fully integrate these types of large datasets in a manner that provides clinicians and patients with meaningful, actionable information. However, if challenges are addressed, precision medicine has the capacity to transform patient care.

No abstract available.