In Vitro-In Vivo Correlation for the Degradation of Tetra-PEG Hydrogel Microspheres with Tunable β-Eliminative Crosslink Cleavage Rates

The degradation of Tetra-PEG hydrogels containing β-eliminative crosslinks has been studied in order to provide an in vitro-in vivo correlation for the use of these hydrogels in our chemically controlled drug delivery system. We measured time-dependent gel mass loss and ultrasound volume changes of 13 subcutaneously implanted Tetra-PEG hydrogel microspheres having degradation times ranging from ~3 to 250 days. Applying a previously developed model of Tetra-PEG hydrogel degradation, the mass changes correlate well with the in vitro rates of crosslink cleavage and hydrogel degelation. These results allow prediction of in vivo biodegradation properties of these hydrogels based on readily obtained in vitro rates, despite having degradation times that span 2 orders of magnitude. These results support the optimization of drug-releasing hydrogels and their development into long-acting therapeutics. The use of ultrasound volume measurements further provides a noninvasive technique for monitoring hydrogel degradation in the subcutaneous space

Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80-90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .</p

A Hydrogel-Microsphere Drug Delivery System That Supports Once-Monthly Administration of a GLP‑1 Receptor Agonist

We have developed a chemically controlled very long-acting delivery system to support once-monthly administration of a peptidic GLP-1R agonist. Initially, the prototypical GLP-1R agonist exenatide was covalently attached to hydrogel microspheres by a self-cleaving β-eliminative linker; after subcutaneous injection in rats, the peptide was slowly released into the systemic circulation. However, the short serum exenatide half-life suggested its degradation in the subcutaneous depot. We found that exenatide undergoes deamidation at Asn²⁸ with an <i>in vitro</i> and <i>in vivo</i> half-life of approximately 2 weeks. The [Gln²⁸]­exenatide variant and exenatide showed indistinguishable GLP-1R agonist activities as well as pharmacokinetic and pharmacodynamic effects in rodents; however, unlike exenatide, [Gln²⁸]­exenatide is stable for long periods. Two different hydrogel-[Gln²⁸]­exenatide conjugates were prepared using β-eliminative linkers with different cleavage rates. After subcutaneous injection in rodents, the serum half-lives for the released [Gln²⁸]­exenatide from the two conjugates were about 2 weeks and one month. Two monthly injections of the latter in the Zucker diabetic fatty rat showed pharmacodynamic effects indistinguishable from two months of continuously infused exenatide. Pharmacokinetic simulations indicate that the delivery system should serve well as a once-monthly GLP-1R agonist for treatment of type 2 diabetes in humans

Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data.

Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80-90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk