Muscle mass affects paclitaxel systemic exposure and may inform personalized paclitaxel dosing

AIMS

Patients with low muscle mass have increased risk of paclitaxel-induced peripheral neuropathy, which is dependent on systemic paclitaxel exposure. Dose optimization may be feasible through the secondary use of radiologic data for body composition. The objective of this study was to interrogate morphomic parameters as predictors of paclitaxel pharmacokinetics to identify alternative dosing strategies that may improve treatment outcomes.

METHODS

This was a secondary analysis of female patients with breast cancer scheduled to receive 80 mg/m² weekly paclitaxel infusions. Paclitaxel was measured at the end of initial infusion to estimate maximum concentration (C_max ). Computed tomography (CT) scans were used to measure 29 body composition features for inclusion in pharmacokinetic modelling. Monte Carlo simulations were performed to identify infusion durations that limit the probability of exceeding C_max  > 2885 ng/mL, which was selected based on prior work linking this to an unacceptable risk of peripheral neuropathy.

RESULTS

Thirty-nine patients were included in the analysis. The optimal model was a two-compartment pharmacokinetic model with T11 skeletal muscle area as a covariate of paclitaxel volume of distribution (Vd). Simulations suggest that extending infusion of the standard paclitaxel dose from 1 hour to 2 and 3 hours in patients who have skeletal muscle area 4907-7080 mm² and <4907 mm² , respectively, would limit risk of C_max  > 2885 ng/mL to <50%, consequently reducing neuropathy, while marginally increasing overall systemic paclitaxel exposure.

CONCLUSION

Extending paclitaxel infusion duration in ~25% of patients who have low skeletal muscle area is predicted to reduce peripheral neuropathy while maintaining systemic exposure, suggesting that personalizing paclitaxel dosing based on body composition may improve treatment outcomes.

Developing a morphomics framework to optimize implant site-specific design parameters for islet macroencapsulation devices

Delivering a clinically impactful cell number is a major design challenge for cell macroencapsulation devices for Type 1 diabetes. It is important to understand the transplant site anatomy to design a device that is practical and that can achieve a sufficient cell dose. We identify the posterior rectus sheath plane as a potential implant site as it is easily accessible, can facilitate longitudinal monitoring of transplants, and can provide nutritive support for cell survival. We have investigated this space using morphomics across a representative patient cohort (642 participants) and have analysed the data in terms of gender, age and BMI. We used a shape optimization process to maximize the volume and identified that elliptical devices achieve a clinically impactful cell dose while meeting device manufacture and delivery requirements. This morphomics framework has the potential to significantly influence the design of future macroencapsulation devices to better suit the needs of patients.

Morphomics Can Predict Oncological Features and Survival of Metastatic Renal Cell Carcinoma After Cytoreductive Nephrectomy

BACKGROUND/AIM

This study analyzed the ability of body composition to predict the outcome of patients with metastatic renal cell carcinoma (RCC) who received cytoreductive nephrectomy followed by systemic therapy.

PATIENTS AND METHODS

A retrospective study was conducted from December 2010 to November 2017 in a single tertiary medical center. The medical charts and computed tomography images were reviewed. Statistical analysis included oncological features, their correlation with body composition factors, and overall survival.

RESULTS

Skeletal muscle volume was significantly higher in patients with Fuhrman grade 2 RCC than those with grade≥3. Patients with intermediate International Metastatic RCC Database Consortium risk had significantly higher BMI and skeletal muscle compared to those with poor risk. Multivariate analysis showed that increased skeletal muscle and decreased visceral adipose tissue were significant predictors of a better overall survival.

CONCLUSION

Body composition highly correlated with the oncological features of metastatic RCC and impacted survival.

Pre-Disease and Pre-Surgery BMI, Weight Loss and Sarcopenia Impact Survival of Resected Lung Cancer Independently of Tumor Stage

Lower pre-surgery Body Mass Index (BMI) and low muscle mass impact negatively long-term survival of non-small cell lung cancer (NSCLC). We investigated their influence on survival after major lung resection for NSCLC.

METHODS

A retrospective analysis of a prospectively collected database was made on 304 consecutive patients.

RESULTS

Underweight, normal, overweight and obese patients represented 7.6%, 51.6%, 28.6%, and 12.6% of the pre-disease population. Weight loss and gain were recorded in 5% and 44.4% of patients, respectively. Low muscle mass was more frequently associated with BMI < 25 kg/m² (p < 0.000001). Overall survival was positively affected by pre-disease (p = 0.036) and pre-surgery (p = 0.017) BMI > 25 kg/m², and, even more, in case of BMI > 25 kg/m² and increasing weight (p = 0.012). Long-term outcome was negatively influenced by low muscle mass (p = 0.042) and weight loss (p = 0.0052) as well as age (p = 0.017), ASA categories (p = 0.025), extent of resection (p = 0.0001), pleural invasion (p = 0.0012) and higher pathologic stage (p < 0.0001). Three stepwise multivariable models confirmed the independent favorable prognostic value of higher pre-disease (RR 0.66[0.49-0.89], p = 0.006) and pre-surgery BMI (RR 0.72[0.54-0.98], p = 0.034), and the absence of low muscle mass (RR 0.56[0.37-0.87], p = 0.0091).

CONCLUSIONS

Body reserves assessed by simple clinical markers impact survival of surgically treated NSCLC. Strategies improving body fat and muscular mass before surgery should be considered.

Measurement of Skeletal Muscle Area Improves Estimation of Aminoglycoside Clearance across Body Size

A consistent approach to the dosing of aminoglycosides across the modern body size distribution has been elusive. We evaluated whether radiologically derived measures of body composition could explain more of the interpatient variability in aminoglycoside pharmacokinetics (PK) than standard body size metrics. This retrospective study included adult patients treated with gentamicin or tobramycin with at least three drug concentrations and computed tomography (CT) imaging available. Aminoglycoside volume and clearance (CL) estimates were computed using a two-compartment model by Bayesian analysis. Morphomic data were extracted from CT images using a custom algorithm. Bivariable and multivariable linear regression were used to assess relationships between PK parameters and covariates. A total of 335 patients were included with a median (minimum, maximum) of 4 (3, 16) aminoglycoside concentrations per patient. The median (minimum, maximum) age, height, and weight of included patients were 57 (21, 93) years, 170 (145, 203) centimeters, and 81 (42, 187) kilograms. Both standard and morphomic measures poorly explained variability in volume (R² < 0.06). Skeletal muscle area and volume explained more of the interpatient variability in CL than weight or sex. Higher precision was observed using a modified Cockcroft-Gault equation with skeletal muscle area at L3 (R²= 0.38) or L4 (R²= 0.37) than the standard Cockcroft-Gault equation using lean (R²= 0.23), adjusted (R²= 0.23), or total (R²= 0.22) body weights. These results highlight that skeletal muscle measurements from CT images obtained in the course of care can improve the precision of aminoglycoside CL estimation over current body size scalars.

Relationships of Vancomycin Pharmacokinetics to Body Size and Composition Using a Novel Pharmacomorphomic Approach Based on Medical Imaging

Antibiotics such as vancomycin are empirically dosed on the basis of body weight, which may not be optimal across the expanding adult body size distribution. Our aim was to compare the relationships between morphomic parameters generated from computed tomography images to conventional body size metrics as predictors of vancomycin pharmacokinetics (PK). This single-center retrospective study included 300 patients with 1,622 vancomycin concentration (52% trough) measurements. Bayesian estimation was used to compute individual vancomycin volume of distribution of the central compartment (Vc) and clearance (CL). Approximately 45% of patients were obese with an overall median (5th, 95th percentile) weight and body mass index of 87.2 (54.7, 123) kg and 28.8 (18.9, 43.7) kg/m², respectively. Morphomic parameters of body size such as body depth, total body area, and torso volume of the twelfth thoracic through fourth lumbar vertebrae (T12 to L4) correlated with Vc. The relationship of vancomycin Vc was poorly predicted by body size but was stronger with T12-to-L4 torso volume (coefficient of determination [R²] = 0.11) than weight (R² = 0.04). No relationships between vancomycin CL and traditional body size metrics could be discerned; however, relationships with skeletal muscle volume and total psoas area were found. Vancomycin CL independently correlated with total psoas area and inversely correlated with age. Thus, vancomycin CL was significantly related to total psoas area over age (R² = 0.23, P < 0.0001). This proof-of-concept study suggests a potential role for translation of radiographic information into parameters predictive of drug pharmacokinetics. Prediction of individual antimicrobial pharmacokinetic parameters using analytic morphomics has the potential to improve antimicrobial dose selection and outcomes of obese patients.

Quantifying Sarcopenia Reference Values Using Lumbar and Thoracic Muscle Areas in a Healthy Population

BACKGROUND

Sarcopenia is defined as the loss of skeletal muscle mass and function associated with aging. Muscle mass can be reliably and accurately quantified using clinical CT scans but reference measurements are lacking, particularly in healthy US populations.

METHODS

Two-phase CT scans from healthy kidney donors (age 18-40) at the University of Michigan between 1999-2010 were utilized. Muscle mass was quantified using two thoracic and two lumbar muscle cross-sectional area (CSA) measures. Indexed measurements were computed as area divided by height-squared. Paired analyses of non-contrast and contrast phases and different Hounsfield Unit (HU) ranges for muscle were conducted to determine their effect on CSA muscle measures. We report the means, standard deviations, and 2SD sarcopenia cutoffs from this population.

RESULTS

Healthy population CSA (cm2) cutoffs for N=604 males/females respectively were: 34.7/20.9 (T12 Dorsal Muscle), 91.5/55.9 (T12 Skeletal Muscle), 141.7/91.2 (L3 Skeletal Muscle), 23.5/14.3 (L4 Total Psoas Area), and 23.4/14.3 (L4 Psoas Muscle Area). Height-indexed CSA (cm2/m2) cutoffs for males/females respectively were: 10.9/7.8 (T12 Dorsal Muscle), 28.7/20.6 (T12 Skeletal Muscle), 44.6/34.0 (L3 Skeletal Muscle), 7.5/5.2 (L4 Total Psoas Area), and 7.4/5.2 (L4 Psoas Muscle Area). We confirmed that a mask of -29 to 150 HU is optimal and shows no significant difference between contrast-enhanced and non-contrast CT scan CSA measurements.

CONCLUSIONS

We quantified reference values for lumbar and thoracic muscle CSA measures in a healthy US population. We defined the effect of IV contrast and different HU ranges for muscle. Combined, these results facilitate the extraction of clinically valuable data from the large numbers of existing scans performed for medical indications.

From gross anatomy to the nanomorphome: stereological tools provide a paradigm for advancing research in quantitative morphomics

The terms morphome and morphomics are not new but, recently, a group of morphologists and cell biologists has given them clear definitions and emphasised their integral importance in systems biology. By analogy to other '-omes', the morphome refers to the distribution of matter within 3-dimensional (3D) space. It equates to the totality of morphological features within a biological system (virus, single cell, multicellular organism or populations thereof) and morphomics is the systematic study of those structures. Morphomics research has the potential to generate 'big data' because it includes all imaging techniques at all levels of achievable resolution and all structural scales from gross anatomy and medical imaging, via optical and electron microscopy, to molecular characterisation. As with other '-omics', quantification is an important part of morphomics and, because biological systems exist and operate in 3D space, precise descriptions of form, content and spatial relationships require the quantification of structure in 3D. Revealing and quantifying structural detail inside the specimen is achieved currently in two main ways: (i) by some form of reconstruction from serial physical or tomographic slices or (ii) by using randomly-sampled sections and simple test probes (points, lines, areas, volumes) to derive stereological estimates of global and/or individual quantities. The latter include volumes, surfaces, lengths and numbers of interesting features and spatial relationships between them. This article emphasises the value of stereological design, sampling principles and estimation tools as a template for combining with alternative imaging techniques to tackle the 'big data' issue and advance knowledge and understanding of the morphome. The combination of stereology, TEM and immunogold cytochemistry provides a practical illustration of how this has been achieved in the sub-field of nanomorphomics. Applying these quantitative tools/techniques in a carefully managed study design offers us a deeper appreciation of the spatiotemporal relationships between the genome, metabolome and morphome which are integral to systems biology.

Systems biology in 3D space--enter the morphome

Systems-based understanding of living organisms depends on acquiring huge datasets from arrays of genes, transcripts, proteins, and lipids. These data, referred to as 'omes', are assembled using 'omics' methodologies. Currently a comprehensive, quantitative view of cellular and organellar systems in 3D space at nanoscale/molecular resolution is missing. We introduce here the term 'morphome' for the distribution of living matter within a 3D biological system, and 'morphomics' for methods of collecting 3D data systematically and quantitatively. A sampling-based approach termed stereology currently provides rapid, precise, and minimally biased morphomics. We propose that stereology solves the 'big data' problem posed by emerging wide-scale electron microscopy (EM) and can establish quantitative links between the newer nanoimaging platforms such as electron tomography, cryo-EM, and correlative microscopy.