Lost in space: design of experiments and scientific exploration in a Hogarth Universe

Critical Design Strategies Supporting Optimized Drug Release from Polymer-Drug Conjugates

The importance of an adequate linking moiety design that allows controlled drug(s) release at the desired site of action is extensively studied for polymer-drug conjugates (PDCs). Redox-responsive self-immolative linkers bearing disulfide moieties (SS-SIL) represent a powerful strategy for intracellular drug delivery; however, the influence of drug structural features and linker-associated spacers on release kinetics remains relatively unexplored. The influence of drug/spacer chemical structure and the chemical group available for conjugation on drug release and the biological effect of resultant PDCs is evaluated. A "design of experiments" tool is implemented to develop a liquid chromatography-mass spectrometry method to perform the comprehensive characterization required for this systematic study. The obtained fit-for-purpose analytical protocol enables the quantification of low drug concentrations in drug release studies and the elucidation of metabolite presence. and provides the first data that clarifies how drug structural features influence the drug release from SS-SIL and demonstrates the non-universal nature of the SS-SIL. The importance of rigorous linker characterization in understanding structure-function correlations between linkers, drug chemical functionalities, and in vitro release kinetics from a rationally-designed polymer-drug nanoconjugate, a critical strategic crafting methodology that should remain under consideration when using a reductive environment as an endogenous drug release trigger.

DoE-Assisted Development of a 2H-MoS2 -Catalyzed Approach for the Production of Indole Derivatives

2H-MoS2 is an appealing semiconductor because of its Earth-abundant nature, cheapness, and low toxicity. This material has shown promising catalytic activity for various energy-related processes, but its use in catalysis for C-C bond forming reactions towards useful organic compounds is still largely unexplored. The lack of examples in organic synthesis is mainly due to the intrinsic difficulties of using bulk 2H-MoS2 (e. g., low surface area), which implies the reliance on high catalytic loadings for obtaining acceptable yields. This makes the optimization process more expensive and tedious. Here, we report the development of a 2H-MoS2 -mediated synthesis of valuable bis(indolyl)methane derivatives, using indoles and benzaldehydes as starting materials. Exploiting the Design of Experiments (DoE) method, we identified the critical parameters affecting the catalytic performance of commercial 2H-MoS2 powder and optimized the reaction conditions. Lastly, we demonstrated that the catalytic system has versatility and good tolerance towards functional group variations of the reagents.

Why Humble Farmers May in Fact Grow Bigger Potatoes: A Call for Street-Smart Decision-Making in Sport

BACKGROUND

The main task of applied sport science is to inform decision-making in sports practice, that is, enabling practitioners to compare the expectable outcomes of different options (e.g. training programs).

MAIN BODY

The "evidence" provided may range from group averages to multivariable prediction models. By contrast, many decisions are still largely based on the subjective, experience-based judgement of athletes and coaches. While for the research scientist this may seem "unscientific" and even "irrational", it is important to realize the different perspectives: science values novelty, universal validity, methodological rigor, and contributions towards long-term advancement. Practitioners are judged by the performance outcomes of contemporary, specific athletes. This makes out-of-sample predictive accuracy and robustness decisive requirements for useful decision support. At this point, researchers must concede that under the framework conditions of sport (small samples, multifactorial outcomes etc.) near certainty is unattainable, even with cutting-edge methods that might theoretically enable near-perfect accuracy. Rather, the sport ecosystem favors simpler rules, learning by experience, human judgement, and integration across different sources of knowledge. In other words, the focus of practitioners on experience and human judgement, complemented-but not superseded-by scientific evidence is probably street-smart after all. A major downside of this human-driven approach is the lack of science-grade evaluation and transparency. However, methods are available to merge the assets of data- and human-driven strategies and mitigate biases.

SHORT CONCLUSION

This work presents the challenges of learning, forecasting and decision-making in sport as well as specific opportunities for turning the prevailing "evidence vs. eminence" contrast into a synergy.

A Customized Bayesian Algorithm to Optimize Enzyme-Catalyzed Reactions

Design of experiments (DoE) plays an important role in optimizing the catalytic performance of chemical reactions. The most commonly used DoE relies on the response surface methodology (RSM) to model the variable space of experimental conditions with the fewest number of experiments. However, the RSM leads to an exponential increase in the number of required experiments as the number of variables increases. Herein we describe a Bayesian optimization algorithm (BOA) to optimize the continuous parameters (e.g., temperature, reaction time, reactant and enzyme concentrations, etc.) of enzyme-catalyzed reactions with the aim of maximizing performance. Compared to existing Bayesian optimization methods, we propose an improved algorithm that leads to better results under limited resources and time for experiments. To validate the versatility of the BOA, we benchmarked its performance with biocatalytic C-C bond formation and amination for the optimization of the turnover number. Gratifyingly, up to 80% improvement compared to RSM and up to 360% improvement vs previous Bayesian optimization algorithms were obtained. Importantly, this strategy enabled simultaneous optimization of both the enzyme's activity and selectivity for cross-benzoin condensation.

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.

A Brief Introduction to Chemical Reaction Optimization

From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

Response Surface Methodology to Efficiently Optimize Intracellular Delivery by Photoporation

Photoporation is an up-and-coming technology for the gentle and efficient transfection of cells. Inherent to the application of photoporation is the optimization of several process parameters, such as laser fluence and sensitizing particle concentration, which is typically done one factor at a time (OFAT). However, this approach is tedious and runs the risk of missing a global optimum. Therefore, in this study, we explored whether response surface methodology (RSM) would allow for more efficient optimization of the photoporation procedure. As a case study, FITC-dextran molecules of 500 kDa were delivered to RAW264.7 mouse macrophage-like cells, making use of polydopamine nanoparticles (PDNPs) as photoporation sensitizers. Parameters that were varied to obtain an optimal delivery yield were PDNP size, PDNP concentration and laser fluence. Two established RSM designs were compared: the central composite design and the Box-Behnken design. Model fitting was followed by statistical assessment, validation, and response surface analysis. Both designs successfully identified a delivery yield optimum five- to eight-fold more efficiently than when using OFAT methodology while revealing a strong dependence on PDNP size within the design space. In conclusion, RSM proves to be a valuable approach to efficiently optimize photoporation conditions for a particular cell type.

Flow chemistry for process optimisation using design of experiments

Implementing statistical training into undergraduate or postgraduate chemistry courses can provide high-impact learning experiences for students. However, the opportunity to reinforce this training with a combined laboratory practical can significantly enhance learning outcomes by providing a practical bolstering of the concepts. This paper outlines a flow chemistry laboratory practical for integrating design of experiments optimisation techniques into an organic chemistry laboratory session in which students construct a simple flow reactor and perform a structured series of experiments followed by computational processing and analysis of the results.

Statistical Design of Experiments for Synthetic Biology

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

Optimization of the Microwave Assisted Glycosylamines Synthesis Based on a Statistical Design of Experiments Approach

Glycans carry a vast range of functions in nature. Utilizing their properties and functions in form of polymers, coatings or glycan derivatives for various applications makes the synthesis of modified glycans crucial. Since amines are easy to modify for subsequent reactions, we investigated regioselective amination conditions of different saccharides. Amination reactions were performed according to Kochetkov and Likhoshertov and accelerated by microwave irradiation. We optimized the synthesis of glycosylamines for N-acetyl-d-galactosamine, d-lactose, d-glucuronic acid and l-(-)-fucose using the design of experiments (DoE) approach. DoE enables efficient optimization with limited number of experimental data. A DoE software generated a set of experiments where reaction temperature, concentration of carbohydrate, nature of aminating agent and solvent were investigated. We found that the synthesis of glycosylamines significantly depends on the nature of the carbohydrate and on the reaction temperature. There is strong indication that high temperatures are favored for the amination reaction.

Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling

Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map gene expression levels and provide biosensors with enhanced performance. This methodology was applied to two biosensors that respond to catabolic breakdown products of lignin biomass, protocatechuic acid and ferulic acid. Utilizing DoE we systematically modified biosensor dose-response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (>500-fold), expanding the sensing range (∼4-orders of magnitude), increasing sensitivity (by >1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose-response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.

A Design of Experiments (DoE) Approach Accelerates the Optimization of Copper-Mediated 18F-Fluorination Reactions of Arylstannanes

Recent advancements in ¹⁸F radiochemistry, such as the advent of copper-mediated radiofluorination (CMRF) chemistry, have provided unprecedented access to novel chemically diverse PET probes; however, these multicomponent reactions have come with a new set of complex optimization problems. Design of experiments (DoE) is a statistical approach to process optimization that is used across a variety of industries. It possesses a number of advantages over the traditionally employed "one variable at a time" (OVAT) approach, such as increased experimental efficiency as well as an ability to resolve factor interactions and provide detailed maps of a process's behavior. Here we demonstrate the utility of DoE to the development and optimization of new radiochemical methodologies and novel PET tracer synthesis. Using DoE to construct experimentally efficient factor screening and optimization studies, we were able to identify critical factors and model their behavior with more than two-fold greater experimental efficiency than the traditional OVAT approach. Additionally, the use of DoE allowed us to glean new insights into the behavior of the CMRF of a number of arylstannane precursors. This information has guided our decision-making efforts while developing efficient reaction conditions that suit the unique process requirements of ¹⁸F PET tracer synthesis.

Extension of quality-by-design concept to the early development phase of pharmaceutical R&D processes

Here, we propose the extension of the quality-by-design (QbD) concept to also fit the early development phases of pharmaceuticals by adding elements that are currently widely applied, but not yet included in the QbD model in a structured way. These are the introduction of a 'zero' preformulation phase (i.e., selection of drug substance, possible dosage forms and administration routes based on the evaluated therapeutic need); building in stakeholders' (industry, patient, and regulatory) requirements into the quality target product profile (QTTP); and the use of modern quality management tools during the composition and process design phase [collecting critical quality attributes (CQAs) and selection of CPPs) for (still laboratory-scale) design space (DS) development. Moreover, during industrial scale-up, CQAs (as well as critical process parameters; CPPs) can be changed; however, we recommend that the existing QbD elements are reconsidered and updated after this phase.