Networking chemical robots for reaction multitasking

The development of the internet of things has led to an explosion in the number of networked devices capable of control and computing. However, whilst common place in remote sensing, these approaches have not impacted chemistry due to difficulty in developing systems flexible enough for experimental data collection. Herein we present a simple and affordable (<$500) chemistry capable robot built with a standard set of hardware and software protocols that can be networked to coordinate many chemical experiments in real time. We demonstrate how multiple processes can be done with two internet connected robots collaboratively, exploring a set of azo-coupling reactions in a fraction of time needed for a single robot, as well as encoding and decoding information into a network of oscillating reactions. The system can also be used to assess the reproducibility of chemical reactions and discover new reaction outcomes using game playing to explore a chemical space

Digitizing chemical discovery with a Bayesian explorer for interpreting reactivity data

Interpreting the outcome of chemistry experiments consistently is slow and frequently introduces unwanted hidden bias. This difficulty limits the scale of collectable data and often leads to exclusion of negative results, which severely limits progress in the field. What is needed is a way to standardize the discovery process and accelerate the interpretation of high-dimensional data aided by the expert chemist’s intuition. We demonstrate a digital Oracle that interprets chemical reactivity using probability. By carrying out >500 reactions covering a large space and retaining both the positive and negative results, the Oracle was able to rediscover eight historically important reactions including the aldol condensation, Buchwald–Hartwig amination, Heck, Mannich, Sonogashira, Suzuki, Wittig, and Wittig–Horner reactions. This paradigm for decoding reactivity validates and formalizes the expert chemist’s experience and intuition, providing a quantitative criterion of discovery scalable to all available experimental data

Discovering new chemistry with an autonomous robotic platform driven by a reactivity-seeking neural network

We present a robotic chemical discovery system capable of navigating a chemical space based on a learned general association between molecular structures and reactivity, while incorporating a neural network model that can process data from online analytics and assess reactivity without knowing the identity of the reagents. Working in conjunction with this learned knowledge, our robotic platform is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown chemical spaces, regardless of the identity of the starting materials. Through the system, we identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, and some unpredictable reactions that yielded new molecules. The validation of the system was done within a budget of 15 inputs combined in 1018 reactions, further analysis of which allowed us to discover not only a new photochemical reaction but also a new reactivity mode for a well-known reagent (p-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of 6 equiv of TosMIC in a “multistep, single-substrate” cascade reaction yielding a trimeric product in high yield (47% unoptimized) with the formation of five new C–C bonds involving sp–sp2 and sp–sp3 carbon centers. An analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of a reactivity-first robotic discovery of unknown reaction methodologies without requiring human input

An integrated self-optimizing programmable chemical synthesis and reaction engine

Robotic platforms for chemistry are developing rapidly but most systems are not currently able to adapt to changing circumstances in real-time. We present a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, we demonstrate the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures. We also show how the use of in-line spectroscopy such as HPLC, Raman, and NMR can be used for closed-loop optimization of reactions, exemplified using Van Leusen oxazole synthesis, a four-component Ugi condensation and manganese-catalysed epoxidation reactions, as well as two previously unreported reactions, discovered from a selected chemical space, providing up to 50% yield improvement over 25–50 iterations. Finally, we demonstrate an experimental pipeline to explore a trifluoromethylations reaction space, that discovers new molecules

Evaluation of Internal Reference Genes for Quantitative Expression Analysis by Real-Time PCR in Ovine Whole Blood

The use of reference genes is commonly accepted as the most reliable approach to normalize qRT-PCR and to reduce possible errors in the quantification of gene expression. The most suitable reference genes in sheep have been identified for a restricted range of tissues, but no specific data on whole blood are available. The aim of this study was to identify a set of reference genes for normalizing qRT-PCR from ovine whole blood. We designed 11 PCR assays for commonly employed reference genes belonging to various functional classes and then determined their expression stability in whole blood samples from control and disease-stressed sheep. SDHA and YWHAZ were considered the most suitable internal controls as they were stably expressed regardless of disease status according to both geNorm and NormFinder software; furthermore, geNorm indicated SDHA/HPRT, YWHAZ/GAPDH and SDHA/YWHAZ as the best reference gene combinations in control, disease-stressed and combined sheep groups, respectively. Our study provides a validated panel of optimal control genes which may be useful for the identification of genes differentially expressed by qRT-PCR in a readily accessible tissue, with potential for discovering new physiological and disease markers and as a tool to improve production traits (e.g., by identifying expression Quantitative Trait Loci). An additional outcome of the study is a set of intron-spanning primer sequences suitable for gene expression experiments employing SYBR Green chemistry on other ovine tissues and cells

Peri-operative red blood cell transfusion in neonates and infants: NEonate and Children audiT of Anaesthesia pRactice IN Europe: A prospective European multicentre observational study

BACKGROUND: Little is known about current clinical practice concerning peri-operative red blood cell transfusion in neonates and small infants. Guidelines suggest transfusions based on haemoglobin thresholds ranging from 8.5 to 12 g dl-1, distinguishing between children from birth to day 7 (week 1), from day 8 to day 14 (week 2) or from day 15 (≥week 3) onwards. OBJECTIVE: To observe peri-operative red blood cell transfusion practice according to guidelines in relation to patient outcome. DESIGN: A multicentre observational study. SETTING: The NEonate-Children sTudy of Anaesthesia pRactice IN Europe (NECTARINE) trial recruited patients up to 60 weeks' postmenstrual age undergoing anaesthesia for surgical or diagnostic procedures from 165 centres in 31 European countries between March 2016 and January 2017. PATIENTS: The data included 5609 patients undergoing 6542 procedures. Inclusion criteria was a peri-operative red blood cell transfusion. MAIN OUTCOME MEASURES: The primary endpoint was the haemoglobin level triggering a transfusion for neonates in week 1, week 2 and week 3. Secondary endpoints were transfusion volumes, 'delta haemoglobin' (preprocedure - transfusion-triggering) and 30-day and 90-day morbidity and mortality. RESULTS: Peri-operative red blood cell transfusions were recorded during 447 procedures (6.9%). The median haemoglobin levels triggering a transfusion were 9.6 [IQR 8.7 to 10.9] g dl-1 for neonates in week 1, 9.6 [7.7 to 10.4] g dl-1 in week 2 and 8.0 [7.3 to 9.0] g dl-1 in week 3. The median transfusion volume was 17.1 [11.1 to 26.4] ml kg-1 with a median delta haemoglobin of 1.8 [0.0 to 3.6] g dl-1. Thirty-day morbidity was 47.8% with an overall mortality of 11.3%. CONCLUSIONS: Results indicate lower transfusion-triggering haemoglobin thresholds in clinical practice than suggested by current guidelines. The high morbidity and mortality of this NECTARINE sub-cohort calls for investigative action and evidence-based guidelines addressing peri-operative red blood cell transfusions strategies. TRIAL REGISTRATION: ClinicalTrials.gov, identifier: NCT02350348

Programmable autonomous chemical robots for discovery and synthesis

The work presented in this thesis focuses on the development of a platform to explore chemical spaces for reaction discovery and a network of robots for automatic collaboration. We believe that in recent years scientific automated systems have become an invaluable asset in the laboratory, vastly improving the productivity and generally changing the approach to chemistry research in many fields. However, the current implementations are mostly for technical help in repetitive tasks or in optimisation of already known reactions. We envision a closed-loop platform pointed towards the unknown, able to automatically perform reactions, analyse them, and use the acquired data to navigate a chemical space and make discoveries. The platform configuration went through a series of gradual improvements and expansions: it started with a single reactor and 4 reagents to finish with 6 parallel reactors, 20 reagents, an inert atmosphere line and three LEDs for photochemical reactions. As analysis equipment we used three benchtop instruments: NMR, MS and IR. All of them were automatically controlled and the data produced was processed using two different algorithms for reactivity assessment: a features extractor and a neural network. Chemical space exploration was also simulated using a neural network correlating the reaction parameters with the reactivity. The system has been used for three applications of increasing complexity. The first one was a reaction optimization performed with two different approaches: a polynomial regressor model and a genetic algorithm. The second task was the exploration of a simple chemical space made of 6 organic molecules and a base. Lastly, the system explored a larger chemical space built to discover new photochemical reactions. By using real time analysis, it was possible to identify promising candidates and successfully discover a new multicomponent reaction, a new photochemical reaction, and re-discover various examples already known in literature. The most interesting reaction has also been investigated manually in order to test its robustness. As a parallel project this thesis will also tackle the science reproducibility problem. We believe that chemistry automation will ensure a better preservation of scientific data, since the experiments’ parameters will be exchanged as defined robotic operations instead of ambiguous manual procedures. Expanding this concept one step forward we imagine a future where laboratory machines are interconnected through the internet, allowing automatic collaboration and data sharing. As a proof of concept we built a network of physically separated robots and showed how it is possible to use internet communication to search an azo-dye chemical space in a fraction of time as well as encoding and decoding information into a network of oscillating reactions

Programmable autonomous chemical robots for discovery and synthesis

The work presented in this thesis focuses on the development of a platform to explore chemical spaces for reaction discovery and a network of robots for automatic collaboration. We believe that in recent years scientific automated systems have become an invaluable asset in the laboratory, vastly improving the productivity and generally changing the approach to chemistry research in many fields. However, the current implementations are mostly for technical help in repetitive tasks or in optimisation of already known reactions. We envision a closed-loop platform pointed towards the unknown, able to automatically perform reactions, analyse them, and use the acquired data to navigate a chemical space and make discoveries. The platform configuration went through a series of gradual improvements and expansions: it started with a single reactor and 4 reagents to finish with 6 parallel reactors, 20 reagents, an inert atmosphere line and three LEDs for photochemical reactions. As analysis equipment we used three benchtop instruments: NMR, MS and IR. All of them were automatically controlled and the data produced was processed using two different algorithms for reactivity assessment: a features extractor and a neural network. Chemical space exploration was also simulated using a neural network correlating the reaction parameters with the reactivity. The system has been used for three applications of increasing complexity. The first one was a reaction optimization performed with two different approaches: a polynomial regressor model and a genetic algorithm. The second task was the exploration of a simple chemical space made of 6 organic molecules and a base. Lastly, the system explored a larger chemical space built to discover new photochemical reactions. By using real time analysis, it was possible to identify promising candidates and successfully discover a new multicomponent reaction, a new photochemical reaction, and re-discover various examples already known in literature. The most interesting reaction has also been investigated manually in order to test its robustness. As a parallel project this thesis will also tackle the science reproducibility problem. We believe that chemistry automation will ensure a better preservation of scientific data, since the experiments’ parameters will be exchanged as defined robotic operations instead of ambiguous manual procedures. Expanding this concept one step forward we imagine a future where laboratory machines are interconnected through the internet, allowing automatic collaboration and data sharing. As a proof of concept we built a network of physically separated robots and showed how it is possible to use internet communication to search an azo-dye chemical space in a fraction of time as well as encoding and decoding information into a network of oscillating reactions

Digitizing Chemical Discovery with a Bayesian Explorer for Interpreting Reactivity Data

Interpretating the outcome of chemistry experiments consistently is slow and often introduces unwanted hidden bias. This difficulty limits the scale of collectable data and often leads to exclusion of negative results, which severely limits progress in the field. What is needed is a way to standardise the discovery process and accelerate the interpretation of high dimensional data aided by the expert chemist’s intuition. We demonstrate a digital Oracle that reasons about chemical reactivity using probability. By doing >500 reactions covering a large space and retaining both the positive and negative results the Oracle was able to rediscover eight historically important reactions including the Aldol condensation, Buchwald-Hartwig amination, Heck, Mannich, Sonogashira, Suzuki, Wittig and Wittig-Horner reactions. This new paradigm for decoding reactivity validates and formalizes the expert chemist’s experience and intuition, providing a quantitative criterion of discovery scalable to all available experimental data