Biology subjects

Hill, R.

Publications and source records attributed to Hill, R..

23 records · Page 2Linked to original sources

bioRxiv2019

Optimizing circadian drug infusion schedules towards personalized cancer chronotherapy

AimsPrecision medicine requires accurate technologies for drug administration and proper systems pharmacology approaches for patient data analysis. Here, plasma pharmacokinetics (PK) data of the OPTILIV trial in which cancer patients received oxaliplatin, 5-fluorouracil and irinotecan via chronomodulated schedules delivered by an infusion pump into the hepatic artery were mathematically investigated.\n\nMethodsA pump-to-patient model was designed in order to accurately represent the drug solution dynamics from the pump to the patient blood. It was connected to semi-mechanistic PK models to analyse inter-patient variability in PK parameters.\n\nResultsLarge time delays of up to 1h41 between the actual pump start and the time of drug detection in patient blood was predicted by the model and confirmed by PK data. Sudden delivery spike in the patient artery due to glucose rinse after drug administration accounted for up to 10.7% of the total drug dose. New model-guided delivery profiles were designed to precisely lead to the drug exposure intended by clinicians. Next, the complete mathematical framework achieved a very good fit to individual time-concentration PK profiles and concluded that inter-subject differences in PK parameters was the lowest for irinotecan, intermediate for oxaliplatin and the largest for 5-fluorouracil. Clustering patients according to their PK parameter values revealed two patient subgroups for each drug in which inter-patient variability was largely decreased compared to that in the total population.\n\nConclusionsThis study provides a complete mathematical framework to optimize drug infusion pumps and inform on inter-patient PK variability, a step towards precise and personalized cancer chronotherapy.\n\nAuthor summaryAccuracy and safety of infusion pumps remain a critical issue in the clinics and the development of accurate mathematical models to optimize drug administration though such devices has a key part to play in the advancement of precision medicine. Here, PK data from cancer patient receiving irinotecan, oxaliplatin and 5-fluorouracil into the hepatic artery via an infusion pump was mathematically investigated. A pump-to-patient model was designed and revealed significant inconsistencies between intended drug profiles and actual plasma concentrations. This mathematical model was then used to suggest improved profiles in order to minimise error and optimise delivery. Physiologically-based PK models of the three drugs were then linked to the pump-to-patient model. The whole framework achieved a very good fit to data and allowed quantifying inter-patient variability in PK parameters and linking them to potential clinical biomarkers via patient clustering. The developed methodology improves our understanding of patient-specific drug pharmacokinetics towards personalized drug administration.

systems biology↗

bioRxiv2019

Fentanyl depression of respiration: comparison with heroin and morphine

Background and PurposeFentanyl overdose deaths have reached epidemic levels in North America. Death in opioid overdose invariably results from respiratory depression. In the present work we have characterized how fentanyl depresses respiration and by comparing fentanyl with heroin and morphine, the active breakdown product of heroin, we have sought to determine whether there are factors, in addition to high potency, that contribute to the lethality of fentanyl.\n\nExperimental ApproachRespiration (rate and tidal volume) was measured in awake, freely moving mice by whole body plethysmography\n\nKey ResultsIntravenously administered fentanyl produced more rapid depression of respiration than equipotent doses of heroin or morphine. Fentanyl depressed both respiratory rate and tidal volume, the effect on tidal volume may reflect increased respiratory muscle stiffness. Fentanyl did not depress respiration in opioid receptor knock-out mice. Naloxone, the opioid antagonist widely used to treat opioid overdose, reversed the depression of respiration by morphine more readily than that by fentanyl whereas diprenorphine, a more lipophilic antagonist, was equipotent in reversing fentanyl and morphine depression of respiration. Prolonged treatment with morphine induced tolerance to respiratory depression but the degree of cross tolerance to fentanyl was less than the tolerance to morphine itself.\n\nConclusion and ImplicationsWe propose that several factors (potency, rate of onset, muscle stiffness, lowered sensitivity to naloxone and lowered cross tolerance to morphine) combine to make fentanyl more likely to cause opioid overdose deaths than other commonly abused opioids.

pharmacology and toxicology↗

bioRxiv2019

Semaphorin 7a dependent programs of cell surivival, matrix remodeling, and EMT drive postpartum breast tumor progression

Young women diagnosed with breast cancer (BC) have poor prognosis due to increased rates of metastasis. Additionally, women within 10 years of most recent childbirth at diagnosis are [~]3 times more likely to develop metastasis than age and stage matched nulliparous women. We define these cases as postpartum BC (PPBC) and propose that the unique biology of the postpartum mammary gland drives tumor progression. Our published results revealed roles for SEMA7A in breast tumor cell growth, motility, invasion, and tumor associated-lymphangiogenesis, all of which are also increased in pre-clinical models of PPBC. However, whether SEMA7A drives progression in PPBC remains largely unexplored. Our results presented herein show that silencing of SEMA7A decreases tumor growth in a model of PPBC while overexpression is sufficient to increase growth in nulliparous hosts. Further, we show that SEMA7A promotes multiple known drivers of PPBC progression including tumor associated COX-2 expression and fibroblast-mediated collagen deposition in the tumor microenvironment. Additionally, we show for the first time that SEMA7A expressing cells deposit fibronectin to promote tumor cell survival. Finally, we show that co-expression of SEMA7A/COX-2/FN predicts for poor prognosis in breast cancer patient cohorts. These studies suggest SEMA7A as a key mediator of BC progression and that targeting SEMA7A may open avenues for novel therapeutic strategies.

cancer biology↗

bioRxiv2018

Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal Circuit Tracing

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically constructing more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. We address these shortcomings with a new fast, flexible, learning-free method for sparse segmentation and reconstruction of neural volumes. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (CT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipelines reconstructions, which are of sufficient quality for preliminary biological study. Compared to existing supervised learning methods, it is both significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.

neuroscience↗

bioRxiv2017

Development of a targeted sequencing approach to identify prognostic, predictive and diagnostic markers in paediatric solid tumours

The implementation of personalised medicine in childhood cancers has been limited by a lack of clinically validated multi-target sequencing approaches specific for paediatric solid tumours. In order to support innovative clinical trials in high-risk patients with unmet need, we have developed a clinically relevant targeted sequencing panel spanning 311 kb and comprising 78 genes involved in childhood cancers. A total of 132 samples were used for the validation of the panel, including Horizon Discovery cell blends (n=4), cell lines (n=15), formalin-fixed paraffin embedded (FFPE, n=83) and fresh frozen tissue (FF, n=30) patient samples. Cell blends containing known single nucleotide variants (SNVs, n=528) and small insertion-deletions (indels n=108) were used to define panel sensitivities of [≥]98% for SNVs and [≥]83% for indels [95% CI] and panel specificity of [≥]98% [95% CI] for SNVs. FFPE samples performed comparably to FF samples (n=15 paired). Of 95 well-characterised genetic abnormalities in 33 clinical specimens and 13 cell lines (including SNVs, indels, amplifications, rearrangements and chromosome losses), 94 (98.9%) were detected by our approach. We have validated a robust and practical methodology to guide clinical management of children with solid tumours based on their molecular profiles. Our work demonstrates the value of targeted gene sequencing in the development of precision medicine strategies in paediatric oncology.

genomics↗