Annual Review of Chemical and Biomolecular Engineering - Volume 13, 2022

Natural products are a diverse class of biologically produced compounds that participate in fundamental biological processes such as cell signaling, nutrient acquisition, and interference competition. Unique triple-bond functionalities like isonitriles and alkynes often drive bioactivity and may serve as indicators of novel chemical logic and enzymatic machinery. Yet, the biosynthetic underpinnings of these groups remain only partially understood, constraining the opportunity to rationally engineer biomolecules with these functionalities for applications in pharmaceuticals, bioorthogonal chemistry, and other value-added chemical processes. Here, we focus our review on characterized biosynthetic pathways for isonitrile and alkyne functionalities, their bioorthogonal transformations, and prospects for engineering their biosynthetic machinery for biotechnological applications.

This article reviews recent developments in the applications of machine learning, data-driven modeling, transfer learning, and autonomous experimentation for the discovery, design, and optimization of soft and biological materials. The design and engineering of molecules and molecular systems have long been a preoccupation of chemical and biomolecular engineers using a variety of computational and experimental techniques. Increasingly, researchers have looked to emerging and established tools in artificial intelligence and machine learning to integrate with established approaches in chemical science to realize powerful, efficient, and in some cases autonomous platforms for molecular discovery, materials engineering, and process optimization. This review summarizes the basic principles underpinning these techniques and highlights recent successful example applications in autonomous materials discovery, transfer learning, and multi-fidelity active learning.

Microfluidic devices and systems have entered many areas of chemical engineering, and the rate of their adoption is only increasing. As we approach and adapt to the critical global challenges we face in the near future, it is important to consider the capabilities of flow chemistry and its applications in next-generation technologies for sustainability, energy production, and tailor-made specialty chemicals. We present the introduction of microfluidics into the fundamental unit operations of chemical engineering. We discuss the traits and advantages of microfluidic approaches to different reactive systems, both well-established and emerging, with a focus on the integration of modular microfluidic devices into high-efficiency experimental platforms for accelerated process optimization and intensified continuous manufacturing. Finally, we discuss the current state and new horizons in self-driven experimentation in flow chemistry for both intelligent exploration through the chemical universe and distributed manufacturing.

Single-use technologies have transformed conventional biopharmaceutical manufacturing, and their adoption is increasing rapidly for emerging applications like antibody–drug conjugates and cell and gene therapy products. These disruptive technologies have also had a significant impact during the coronavirus disease 2019 pandemic, helping to advance process development to enable the manufacturing of new monoclonal antibody therapies and vaccines. Single-use systems provide closed plug-and-play solutions and enable process intensification and continuous processing. Several challenges remain, providing opportunities to advance single-use sensors and their integration with single-use systems, to develop novel plastic materials, and to standardize design for interchangeability. Because the industry is changing rapidly, a holistic analysis of the current single-use technologies is required, with a summary of the latest advancements in materials science and the implementation of these technologies in end-to-end bioprocesses.

Interferometry is a highly versatile tool for probing physical and chemical phenomena. In addition to the benefit of noncontact investigations, even spatially resolved information can be obtained by choosing a suitable setup. This review presents the evolution of the various setups that have evolved since the first interferometers were developed in the mid-nineteenth century and highlights the benefits, limitations, and typical areas of application. This review focuses on interferometry based on electromagnetic waves in the near-infrared and visible range applied to liquid samples, categorizes the chemical/physical properties (e.g., pressure, temperature, composition) and phenomena (e.g., evaporation, crystal growth, diffusion, thermophoresis) that can be assessed, and presents a comprehensive literature review of specific existing applications. Finally, it discusses some fundamental open questions with respect to geometric considerations and overlapping effects.

Since late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally, causing a pandemic (coronavirus disease 2019, or COVID-19) with dire consequences, including widespread death, long-term illness, and societal and economic disruption. Although initially uncertain, evidence is now overwhelming that SARS-CoV-2 is transmitted primarily through small respiratory droplets and aerosols emitted by infected individuals. As a result, many effective nonpharmaceutical interventions for slowing virus transmission operate by blocking, filtering, or diluting respiratory aerosol, particularly in indoor environments. In this review, we discuss the evidence for airborne transmission of SARS-CoV-2 and implications for engineering solutions to reduce transmission risk.

This review describes key milestones related to the production of biopharmaceuticals—therapies manufactured using recombinant DNA technology. The market for biopharmaceuticals has grown significantly since the first biopharmaceutical approval in 1982, and the scientific maturity of the technologies used in their manufacturing processes has grown concomitantly. Early processes relied on established unit operations, with research focused on process scale-up and improved culture productivity. In the early 2000s, changes in regulatory frameworks and the introduction of Quality by Design emphasized the importance of developing manufacturing processes to deliver a desired product quality profile. As a result, companies adopted platform processes and focused on understanding the dynamic interplay between product quality and processing conditions. The consistent and reproducible manufacturing processes of today's biopharmaceutical industry have set high standards for product efficacy, quality, and safety, and as the industry continues to evolve in the coming decade, intensified processing capabilities for an expanded range of therapeutic modalities will likely become routine.

Liquid-phase transmission electron microscopy (LPTEM) is a powerful in situ visualization technique for directly characterizing nanomaterials in the liquid state. Despite its successful application in many fields, several challenges remain in achieving more accurate and reliable observations. We present LPTEM in chemical and biological applications, including studies for the morphological transformation and dynamics of nanoparticles, battery systems, catalysis, biomolecules, and organic systems. We describe the possible interactions and effects of the electron beam on specimens during observation and present sample-specific approaches to mitigate and control these electron-beam effects. We provide recent advances in achieving atomic-level resolution for liquid-phase investigation of structures anddynamics. Moreover, we discuss the development of liquid cell platforms and the introduction of machine-learning data processing for quantitative and objective LPTEM analysis.

T cells engineered to express chimeric antigen receptors (CARs) have shown remarkable success in treating B-cell malignancies, reflected by multiple US Food and Drug Administration–approved CAR-T cell products currently on the market. However, various obstacles have thus far limited the use of approved products and constrained the efficacy of CAR-T cell therapy against solid tumors. Overcoming these obstacles will necessitate multidimensional CAR-T cell engineering approaches and better understanding of the intricate tumor microenvironment (TME). Key challenges include treatment-related toxicity, antigen escape and heterogeneity, and the highly immunosuppressive profile of the TME. Notably, the hypoxic and nutrient-deprived nature of the TME severely attenuates CAR-T cell fitness and efficacy, highlighting the need for more sophisticated engineering strategies. In this review, we examine recent advances in protein- and cell-engineering strategies to improve CAR-T cell safety and efficacy, with an emphasis on overcoming immunosuppression induced by tumor metabolism and hypoxia.

Large-scale deployment of negative emissions technologies (NETs) that permanently remove CO2 from the atmosphere is now considered essential for limiting the global temperature increase to less than 2°C by the end of this century. One promising NET is direct air capture (DAC), a technology that employs engineered chemical processes to remove atmospheric carbon dioxide, potentially at the scale of billions of metric tons per year. This review highlights one of the two main approaches to DAC based on aqueous solvents. The discussion focuses on different aspects of DAC with solvents, starting with the fundamental chemistry that includes the chemical species and reactions involved and the thermodynamics and kinetics of CO2 binding and release. Chemical engineering aspects are also discussed, including air–liquid contactor design, process development, and technoeconomic assessments to estimate the cost of the DAC technologies. Various solvents employed in DAC are reviewed, from aqueous alkaline solutions (NaOH, KOH) to aqueous amines, amino acids, and peptides, along with different solvent regeneration methods, from the traditional thermal swinging to the more exploratory carbonate crystallization with guanidines or electrochemical methods.

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties.

The emergence of human pluripotent stem cell (hPSC) technology over the past two decades has provided a source of normal and diseased human cells for a wide variety of in vitro and in vivo applications. Notably, hPSC-derived cardiomyocytes (hPSC-CMs) are widely used to model human heart development and disease and are in clinical trials for treating heart disease. The success of hPSC-CMs in these applications requires robust, scalable approaches to manufacture large numbers of safe and potent cells. Although significant advances have been made over the past decade in improving the purity and yield of hPSC-CMs and scaling the differentiation process from 2D to 3D, efforts to induce maturation phenotypes during manufacturing have been slow. Process monitoring and closed-loop manufacturing strategies are just being developed. We discuss recent advances in hPSC-CM manufacturing, including differentiation process development and scaling and downstream processes as well as separation and stabilization.

The direct capture of CO2 from ambient air presents a means of decelerating the growth of global atmospheric CO2 concentrations. Considerations relating to process engineering are the focus of this review and have received significantly less attention than those relating to the design of materials for direct air capture (DAC). We summarize minimum thermodynamic energy requirements, second law efficiencies, major unit operations and associated energy requirements, capital and operating expenses, and potential alternative process designs. We also highlight process designs applied toward more concentrated sources of CO2 that, if extended to lower concentrations, could help move DAC units closer to more economical continuous operation. Addressing shortcomings highlighted here could aid in the design of improved DAC processes that overcome trade-offs between capture performance and DAC cost.

There is an urgent need for new technologies to enable circularity for synthetic polymers, spurred by the accumulation of waste plastics in landfills and the environment and the contributions of plastics manufacturing to climate change. Chemical recycling is a promising means to convert waste plastics into molecular intermediates that can be remanufactured into new products. Given the growing interest in the development of new chemical recycling approaches, it is critical to evaluate the economics, energy use, greenhouse gas emissions, and other life cycle inventory metrics for emerging processes,relative to the incumbent, linear manufacturing practices employed today. Here we offer specific definitions for classes of chemical recycling and upcycling and describe general process concepts for the chemical recycling of mixed plastics waste. We present a framework for techno-economic analysis and life cycle assessment for both closed- and open-loop chemical recycling. Rigorous application of these process analysis tools will be required to enable impactful solutions for the plastics waste problem.

Brain disease remains a significant health, social, and economic burden with a high failure rate of translation of therapeutics to the clinic. Nanotherapeutics have represented a promising area of technology investment to improve drug bioavailability and delivery to the brain, with several successes for nanotherapeutic use for central nervous system disease that are currently in the clinic. However, renewed and continued research on the treatment of neurological disorders is critically needed. We explore the challenges of drug delivery to the brain and the ways in which nanotherapeutics can overcome these challenges. We provide a summary and overview of general design principles that can be applied to nanotherapeutics for uptake and penetration in the brain. We next highlight remaining questions that limit the translational potential of nanotherapeutics for application in the clinic. Lastly, we provide recommendations for ongoing preclinical research to improve the overall success of nanotherapeutics against neurological disease.

This article presents a review of the application of blockchain and blockchain-based smart contracts in the chemical and related industries. We introduce the basic concepts of blockchain and smart contracts and explain how some of their features are enabled. We review several typical or novel applications of blockchain and smart contract technologies and their enabling concepts and underlying technologies. We classify the selected literature into five categories and discuss their motivations and technical designs. We recognize that the trend of decentralization creates a need to use blockchain and smart contracts to implement trust and distributed control mechanisms. We also speculate on future applications of blockchain and smart contracts. We believe that, in the future, blockchains with different consensus mechanisms will be studied and applied to achieve more efficient and practical decentralized systems. Also, blockchain-based smart contracts will be more widely applied to enhance autonomous distributed controls in decentralized systems.

Optogenetics has been used in a variety of microbial engineering applications, such as chemical and protein production, studies of cell physiology, and engineered microbe–host interactions. These diverse applications benefit from the precise spatiotemporal control that light affords, as well as its tunability, reversibility, and orthogonality. This combination of unique capabilities has enabled a surge of studies in recent years investigating complex biological systems with completely new approaches. We briefly describe the optogenetic tools that have been developed for microbial engineering, emphasizing the scientific advancements that they have enabled. In particular, we focus on the unique benefits and applications of implementing optogenetic control, from bacterial therapeutics to cybergenetics. Finally, we discuss future research directions, with special attention given to the development of orthogonal multichromatic controls. With an abundance of advantages offered by optogenetics, the future is bright in microbial engineering.

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

This review discusses the complex behaviors in diverse chemical and biochemical systems to elucidate their commonalities and thus help develop a mesoscience methodology to address the complexities in even broader topics. This could possibly build a new scientific paradigm for different disciplines and could meanwhile provide effective tools to tackle the big challenges in various fields, thus paving a path toward combining the paradigm shift in science with the breakthrough in technique developments. Starting with our relatively fruitful understanding of chemical systems, the discussion focuses on the relatively pristine but very intriguing biochemical systems. It is recognized that diverse complexities are multilevel in nature, with each level being multiscale and the complexity emerging always at mesoscales in mesoregimes. Relevant advances in theoretical understandings and mathematical tools are summarized as well based on case studies, and the convergence between physics and mathematics is highlighted.