Streamlining bioactive molecular discovery through integration and automation

The discovery of bioactive small molecules is generally driven via iterative design–make–purify–test cycles. Automation is routinely harnessed at individual stages of these cycles to increase the productivity of drug discovery. Here, we describe recent progress to automate and integrate two or more adjacent stages within discovery workflows. Examples of such technologies include microfluidics, liquid-handling robotics and affinity-selection mass spectrometry. The value of integrated technologies is illustrated in the context of specific case studies in which modulators of targets, such as protein kinases, nuclear hormone receptors and protein–protein interactions, were discovered. We note that to maximize impact on the productivity of discovery, each of the integrated stages would need to have both high and matched throughput. We also consider the longer-term goal of realizing the fully autonomous discovery of bioactive small molecules through the integration and automation of all stages of discovery

DeepDelta: Predicting Pharmacokinetic Improvements of Molecular Derivatives with Deep Learning

Established molecular machine learning models process individual molecules as inputs to predict their biological, chemical, or physical properties. However, such algorithms require large datasets and have not been optimized to predict property differences between molecules. Many drug and material development tasks would benefit from an algorithm that can directly compare two molecules to guide molecular optimization and prioritization. Here, we develop DeepDelta, a pairwise deep learning approach that processes two molecules simultaneously and learns to predict property differences between two molecules from small datasets. On 10 pharmacokinetic benchmark tasks, our DeepDelta approach outperforms two established molecular machine learning algorithms, the message passing neural network (MPNN) ChemProp and Random Forest using radial fingerprints. We further analyze our performance and find that DeepDelta is particularly outperforming established approaches at predicting large differences in molecular properties and can perform scaffold hopping. Furthermore, we derive simple computational tests of our models based on mathematical invariants and show that compliance to these tests correlate with overall model performance – providing an innovative, unsupervised, and easily computable measure of expected model performance and applicability. Taken together, DeepDelta provides an accurate approach to predict molecular property differences and will allow for higher fidelity and transparency in molecular optimization for drug development and the chemical sciences

Person-tailored t cell composition targeting merkel cell carcinoma

The present invention relates to a method for producing a person-tailored T cell composition by in vitro stimulation and expansion of T cells comprising the steps of i) providing at least one identified HLA haplotype from a subject; ii) preparing at least one APC comprising at least one HLA haplotype corresponding to said at least one identified HLA haplotype; and at least one antigenic peptide matched to said at least one HLA haplotype; wherein said at least one antigenic peptide comprises an epitope from Merkel cell polyomavirus, said epitope originates from large T antigen (LTA), small T antigen (STA) or the shared region (CT) of LTA and STA; iii) providing a sample comprising T cells, iv) contacting said sample with an expansion solution comprising at least one APC as prepared in step ii, v) stimulating and expanding T cells with specificity for said at least one antigenic peptide comprised on at least one APC in culture, and optionally harvesting the T cells from the culture, to obtain a person-tailored T cell composition.</p

Unstable impedance of a single electrode contact resulting in loss of DBS therapya case report

yy Open and short circuits of electrode contacts are important technical dysfunctions of DBS. Here, we report on another type of dysfunction restricted to a single electrode contact: impedance instability within regular absolute values. After 9-year subthalamic DBS, a Parkinson patient developed unilateral motor symptoms and intermittent dysaesthesia due to impedance instability of the active contact. DBS efficacy could be restored without surgical revision by activation of the neighboring contact. During 3-year-follow-up, impedances of the dysfunctional contact varied between 1 and 3k whereas the other three contacts remained stable. Impedance documentation is crucial to identify such dysfunctions

Impaired context-sensitive adjustment of behaviour in Parkinson's disease patients tested on and off medication: An fMRI study

The brain's sensitivity to and accentuation of unpredicted over predicted sensory signals plays a fundamental role in learning. According to recent theoretical models of the predictive coding framework, dopamine is responsible for balancing the interplay between bottom-up input and top-down predictions by controlling the precision of surprise signals that guide learning. Using functional MRI, we investigated whether patients with Parkinson's disease (PD) show impaired learning from prediction errors requiring either adaptation or stabilisation of current predictions. Moreover, we were interested in whether deficits in learning over a specific time scale would be accompanied by altered surprise responses in dopamine-related brain structures. To this end, twenty-one PD patients tested on and off dopaminergic medication and twenty-one healthy controls performed a digit prediction paradigm. During the task, violations of sequence-based predictions either signalled the need to update or to stabilise the current prediction and, thus, to react to them or ignore them, respectively. To investigate contextual adaptation to prediction errors, the probability (or its inverse, surprise) of the violations fluctuated across the experiment. When the probability of prediction errors over a specific time scale increased, healthy controls but not PD patients off medication became more flexible, i.e., error rates at violations requiring a motor response decreased in controls but increased in patients. On the neural level, this learning deficit in patients was accompanied by reduced signalling in the substantia nigra and the caudate nucleus. In contrast, differences between the groups regarding the probabilistic modulation of behaviour and neural responses were much less pronounced at prediction errors requiring only stabilisation but no adaptation. Interestingly, dopaminergic medication could neither improve learning from prediction errors nor restore the physiological, neurotypical pattern. Our findings point to a pivotal role of dysfunctions of the substantia nigra and caudate nucleus in deficits in learning from flexibility-demanding prediction errors in PD. Moreover, the data witness poor effects of dopaminergic medication on learning in PD