Chemical Platform Research Articles

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

Regio- and Stereo-Selective Isomerization of Borylated 1,3-Dienes Enabled by Selective Energy Transfer Catalysis.

Configurationally-defined dienes are pervasive across the bioactive natural product spectrum, where they typically manifest themselves as sorbic acid-based fragments. These C5 motifs reflect the biosynthesis algorithms that facilitate their construction. To complement established biosynthetic paradigms, a chemical platform to facilitate the construction of stereochemically defined, functionalizable dienes by light-enabled isomerization has been devised. Enabled by selective energy transfer catalysis, a variety of substituted β-boryl sorbic acid derivatives can be isomerized in a regio- and stereo-selective manner (up to 97 : 3). Directionality is guided by a stabilizing nO→pB interaction in the product: this constitutes a formal anti-hydroboration of the starting alkyne. This operationally simple reaction employs low catalyst loadings (1 mol %) and is complete in 1 h. X-ray analysis supports the hypothesis that the nO→pB interaction leads to chromophore bifurcation: this provides a structural foundation for selective energy transfer.

Regio‐ and Stereo‐Selective Isomerization of Borylated 1,3‐Dienes Enabled by Selective Energy Transfer Catalysis

AbstractConfigurationally‐defined dienes are pervasive across the bioactive natural product spectrum, where they typically manifest themselves as sorbic acid‐based fragments. These C5 motifs reflect the biosynthesis algorithms that facilitate their construction. To complement established biosynthetic paradigms, a chemical platform to facilitate the construction of stereochemically defined, functionalizable dienes by light‐enabled isomerization has been devised. Enabled by selective energy transfer catalysis, a variety of substituted β‐boryl sorbic acid derivatives can be isomerized in a regio‐ and stereo‐selective manner (up to 97 : 3). Directionality is guided by a stabilizing nO→pB interaction in the product: this constitutes a formal anti‐hydroboration of the starting alkyne. This operationally simple reaction employs low catalyst loadings (1 mol %) and is complete in 1 h. X‐ray analysis supports the hypothesis that the nO→pB interaction leads to chromophore bifurcation: this provides a structural foundation for selective energy transfer.

Acid-Catalyzed Activation and Condensation of the =C5H Bond of Furfural on Aldehydes, an Entry Point to Biobased Monomers.

Furfural is an industrially relevant biobased chemical platform. Unlike classical furan, or C-alkylated furans, which have been previously described in the current literature, the =C5H bond of furfural is unreactive. As a result, on a large scale, C=C and C=O bond hydrogenation/hydrogenolysis is mainly performed, with furfuryl alcohol and methyl tetrahydrofuran being the two main downstream chemicals. Here, we show that the derivatization of the -CHO group of furfural restores the reactivity of its =C5H bond, thus permitting its double condensation on various alkyl aldehydes. Overcoming the recalcitrance of the =C5H bond of furfural has opened an access to a biobased monomer, whose potential have been investigated in the fabrication of renewably-sourced poly(silylether). By means of a combined theoretical-experimental study, a reactivity scale for furfural and its protected derivatives against carbonylated compounds has been established using an electrophilicity descriptor, a means to predict the molecular diversity and complexity this pathway may support, and also to de-risk any project related to this topic. Finally, by using performance criteria for industrial operations in the field of fuels and commodities, we discussed the industrial potential of this work in terms of cost, E-factor, reactor productivity and catalyst consumption.

Performance and robustness of small molecule retention time prediction with molecular graph neural networks in industrial drug discovery campaigns

This study explores how machine-learning can be used to predict chromatographic retention times (RT) for the analysis of small molecules, with the objective of identifying a machine-learning framework with the robustness required to support a chemical synthesis production platform. We used internally generated data from high-throughput parallel synthesis in context of pharmaceutical drug discovery projects. We tested machine-learning models from the following frameworks: XGBoost, ChemProp, and DeepChem, using a dataset of 7552 small molecules. Our findings show that two specific models, AttentiveFP and ChemProp, performed better than XGBoost and a regular neural network in predicting RT accurately. We also assessed how well these models performed over time and found that molecular graph neural networks consistently gave accurate predictions for new chemical series. In addition, when we applied ChemProp on the publicly available METLIN SMRT dataset, it performed impressively with an average error of 38.70 s. These results highlight the efficacy of molecular graph neural networks, especially ChemProp, in diverse RT prediction scenarios, thereby enhancing the efficiency of chromatographic analysis.

Abstract 6576: Discovery of potent and selective novel small molecule drugs to target estrogen receptor mutants in endocrine-resistant breast cancer

Abstract Introduction: The acquisition of estrogen receptor alpha (ERα) gene (ESR1) mutations is a key driver for the development of resistance to current endocrine therapy in breast cancer. Clinical studies have shown that ESR1 mutations are frequently observed in metastatic ER-positive breast cancer patients and are associated with poor survival. It is well appreciated that activating ESR1 somatic mutations, especially Y537S and D538G, can drive estrogen-independent activities. In addition, these ESR1 mutations diminish the potency of the current standard-of-care agents (tamoxifen and fulvestrant) that bind ERα directly. Therefore, it is an unmet need to develop next-generation antiestrogens that inhibit ERα mutant signaling in breast cancer to improve patient survival. Here, we search for small molecule inhibitors against ERα mutants Y537S and D538G using DNA-encoded chemical library (DEL). Methods: ERα ligand binding domain (LBD) proteins corresponding to wild type (WT), Y537S and D538G mutants were expressed in E. coli and purified by Ni-NTA, anion exchange, and size exclusion chromatography. The ability of these purified proteins to bind ligands was tested in biochemical assays to validate their use in DEL selections. We conducted a DEL affinity selection using our in-house collection of 6 billion small molecule compounds against WT, Y537S and D538G mutants ER LBD proteins in the presence and absence of estradiol. Selection hits that enriched with these targets were resynthesized off-DNA and tested in biochemical and functional studies using CRISPR-Cas9 knock-in Y537S or D538G mutant breast cancer cells. Results: We have successfully purified microgram amounts of ERα LBD for WT, Y537S, and D538G proteins. In our biochemical assay, the SRC3 peptide binds to the WT ERα LBD in the presence of estradiol, whereas Y537S and D538G LBDs bind the SRC3 peptide in the absence of estradiol, consistent with these mutants constitutively binding to SRC3. Our multibillion small molecule collections of DELs can screen and identify several hits in WT and mutant ERα LBDs. To confirm the selection output, we synthesized off-DNA compounds and validated these in biochemical and cell-based studies. Compounds CDD-1274 and CDD-1802 dramatically decrease the WT and mutant ERα protein levels in multiple ER+ breast cancer cells. These compounds greatly reduce protein levels for GREB1, TFF1, c-MYC, E2F1, and survivin in wildtype and mutant breast cancer cells, and they increase levels of cleaved PARP, a marker for apoptosis in wildtype and mutant ER+ breast cancer cells but not in ER-negative breast cancer cells. Conclusion: Our study rapidly identified potent and novel small molecule drugs to target estrogen receptor mutants using our DNA-encoded chemical library platform. Acknowledgments: Funding sources (NIH/NCI R03 CA259664 and CPRIT RP220524 to MP). Citation Format: Anil Kumar Devakrishnan, Chandrashekhar Madasu, Suzanne A.W Fuqua, Martin M. Matzuk, Murugesan Palaniappan. Discovery of potent and selective novel small molecule drugs to target estrogen receptor mutants in endocrine-resistant breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6576.

Abstract 1826: New inhibitors of oncology targets discovered using the affinity-mediated selection of DNA-encoded chemical libraries followed by machine-learning

Abstract X-Chem’s DNA-Encoded Chemical Library platform generates a large amount of binding data for each target it is screened against. Here, we report the application of machine learning to these large datasets with case studies that include the identification of novel inhibitors of two oncology targets. One, ERα, is an estrogen-activated receptor that controls transcription and stimulates breast cell proliferation including in breast cancer. The second, DCAF1, is the substrate-recognition component of an E3 ligase complex that is associated with the proliferation of colon cancer cells. Over 100 billion DNA-Encoded compounds were synthesized and screened for their affinity to each target. The selection output data was then featurized and used to train multiple machine-learned models. Models with demonstrated predictive power were then used to score virtual catalogue compounds which were subsequently acquired and tested. A range of successful data-science strategies will be described including those that lead to double-digit nM inhibitors of each of DCAF1 and ERα. Citation Format: Anthony D. Keefe, Mohamed AbouZleikha, AJ Baghaie, Matthew A. Clark, Marie-Aude Guie, John P. Guilinger, Ryan Walsh, Ying Zhang. New inhibitors of oncology targets discovered using the affinity-mediated selection of DNA-encoded chemical libraries followed by machine-learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1826.

Assembling the RNA therapeutics toolbox.

From the approval of COVID-19 mRNA vaccines to the 2023 Nobel Prize awarded for nucleoside base modifications, RNA therapeutics have entered the spotlight and aretransforming drug development. While the term "RNA therapeutics" has been used in various contexts, this review focuses on treatments that utilize RNA as a component or target RNA for therapeutic effects. We summarize the latest advances in RNA-targeting tools and RNA-based technologies, including but not limited to mRNA, antisense oligos, siRNAs, small molecules and RNA editors. We focus on the mechanisms of current FDA-approved therapeutics but also provide a discussion on the upcoming workforces. The clinical utility of RNA-based therapeutics is enabled not only by the advances in RNA technologies but in conjunction with the significant improvements in chemical modifications and delivery platforms, which are also briefly discussed in the review. We summarize the latest RNA therapeutics based on their mechanisms and therapeutic effects, which include expressing proteins for vaccination and protein replacement therapies, degrading deleterious RNA, modulating transcription and translation efficiency, targeting noncoding RNAs, binding and modulating protein activity and editing RNA sequences and modifications. This review emphasizes the concept of an RNA therapeutic toolbox, pinpointing the readers to all the tools available for their desired research and clinical goals. As the field advances, the catalog of RNA therapeutic tools continues to grow, further allowing researchers to combine appropriate RNA technologies with suitable chemical modifications and delivery platforms to develop therapeutics tailored to their specific clinical challenges.

Debottlenecking the L-DOPA 4,5-dioxygenase step with enhanced tyrosine supply boosts betalain production in Nicotiana benthamiana.

Synthetic biology provides emerging tools to produce valuable compounds in plant hosts as sustainable chemical production platforms. However, little is known about how supply and utilization of precursors is coordinated at the interface of plant primary and specialized metabolism, limiting our ability to efficiently produce high levels of target specialized metabolites in plants. L-Tyrosine is an aromatic amino acid precursor of diverse plant natural products including betalain pigments, which are used as the major natural food red colorants and more recently a visual marker for plant transformation. Here, we studied the impact of enhanced L-tyrosine supply on the production of betalain pigments by expressing arogenate dehydrogenase (TyrA) from table beet (Beta vulgaris, BvTyrAα), which has relaxed feedback inhibition by L-tyrosine. Unexpectedly, betalain levels were reduced when BvTyrAα was coexpressed with the betalain pathway genes in Nicotiana benthamiana leaves; L-tyrosine and 3,4-dihydroxy-L-phenylalanine (L-DOPA) levels were drastically elevated but not efficiently converted to betalains. An additional expression of L-DOPA 4,5-dioxygenase (DODA), but not CYP76AD1 or cyclo-DOPA 5-O-glucosyltransferase, together with BvTyrAα and the betalain pathway, drastically enhanced betalain production, indicating that DODA is a major rate-limiting step of betalain biosynthesis in this system. Learning from this initial test and further debottlenecking the DODA step maximized betalain yield to an equivalent or higher level than that in table beet. Our data suggest that balancing between enhanced supply ("push") and effective utilization ("pull") of precursor by alleviating a bottleneck step is critical in successful plant synthetic biology to produce high levels of target compounds.

Chemoproteomic Profiling Maps Zinc-Dependent Cysteine Reactivity.

As a vital micronutrient, zinc is integral to the structure, function, and signaling networks of diverse proteins. Dysregulated zinc levels, due to either excess intake or deficiency, are associated with a spectrum of health disorders. In this context, understanding zinc-regulated biological processes at the molecular level holds significant relevance to public health and clinical practice. Identifying and characterizing zinc-regulated proteins in their diverse proteoforms, however, remain a difficult task in advancing zinc biology. Herein, we address this challenge by developing a quantitative chemical proteomics platform that globally profiles the reactivities of proteinaceous cysteines upon cellular zinc depletion. Exploiting a protein-conjugated resin for the selective removal of Zn2+ from culture media, we identify an array of zinc-sensitive cysteines on proteins with diverse functions based on their increased reactivity upon zinc depletion. Notably, we find that zinc regulates the enzymatic activities, post-translational modifications, and subcellular distributions of selected target proteins such as peroxiredoxin 6 (PRDX6), platelet-activating factor acetylhydrolase IB subunit alpha1 (PAFAH1B3), and phosphoglycerate kinase (PGK1).

The effect of gamma radiation on 5-hydroxymethylfurfural conversion in water and dimethyl sulfoxide

Abstract 5-Hydroxymethylfurfural (HMF) is a biomass-based chemical platform that can undergo many feasible reactions. One of the most important reactions is the oxidation to 2,5-furandicarboxylic acid (FDCA), which is the monomer for bioplastic production. In this work, the radiation method was used to investigate the conversion of HMF in both aqueous (DI) and dimethyl sulfoxide (DMSO) solutions. The effects of media solvents, atmospheric gases, HMF concentrations, additive bases, and absorbed doses of gamma radiation were studied. The results showed that the media solvent played a crucial role in HMF conversion under gamma irradiation. At 30 kGy, the HMF conversions in DI and DMSO were 92.1 and 24.1%, respectively, and the oxidation products were only found in the irradiated samples under DMSO. The HMF conversion and oxidation product formation increased with the gamma radiation dose. Moreover, it was found that FDCA stability toward gamma irradiation is highly sensitive in aqueous solution but relatively stable in DMSO. The results implied the alternative promising choice of radiation method compared with traditional methods. To join the bridge, the use of a mixture solvent DI/DMSO seems considerable in the future.

Postpolymerization Modification of Poly(2-vinyl-4,4-dimethyl azlactone) as a Versatile Strategy for Drug Conjugation and Stimuli-Responsive Release.

Postpolymerization modification of highly defined "scaffold" polymers is a promising approach for overcoming the existing limitations of controlled radical polymerization such as batch-to-batch inconsistencies, accessibility to different monomers, and compatibility with harsh synthesis conditions. Using multiple physicochemical characterization techniques, we demonstrate that poly(2-vinyl-4,4-dimethyl azlactone) (PVDMA) scaffolds can be efficiently modified with a coumarin derivative, doxorubicin, and camptothecin small molecule drugs. Subsequently, we show that coumarin-modified PVDMA has a high cellular biocompatibility and that coumarin derivatives are liberated from the polymer in the intracellular environment for cytosolic accumulation. In addition, we report the pharmacokinetics, biodistribution, and antitumor efficacy of a PVDMA-based polymer for the first time, demonstrating unique accumulation patterns based on the administration route (i.e., intravenous vs oral), efficient tumor uptake, and tumor growth inhibition in 4T1 orthotopic triple negative breast cancer (TNBC) xenografts. This work establishes the utility of PVDMA as a versatile chemical platform for producing polymer-drug conjugates with a tunable, stimuli-responsive delivery.

Programming crystallization kinetics of self-assembled DNA crystals with 5-methylcytosine modification

Self-assembled DNA crystals offer a precise chemical platform at the ångström-scale for DNA nanotechnology, holding enormous potential in material separation, catalysis, and DNA data storage. However, accurately controlling the crystallization kinetics of such DNA crystals remains challenging. Herein, we found that atomic-level 5-methylcytosine (5mC) modification can regulate the crystallization kinetics of DNA crystal by tuning the hybridization rates of DNA motifs. We discovered that by manipulating the axial and combination of 5mC modification on the sticky ends of DNA tensegrity triangle motifs, we can obtain a series of DNA crystals with controllable morphological features. Through DNA-PAINT and FRET-labeled DNA strand displacement experiments, we elucidate that atomic-level 5mC modification enhances the affinity constant of DNA hybridization at both the single-molecule and macroscopic scales. This enhancement can be harnessed for kinetic-driven control of the preferential growth direction of DNA crystals. The 5mC modification strategy can overcome the limitations of DNA sequence design imposed by limited nucleobase numbers in various DNA hybridization reactions. This strategy provides a new avenue for the manipulation of DNA crystal structure, valuable for the advancement of DNA and biomacromolecular crystallography.

Coordination nanosheets based molecular computing for detection and differentiation of high-energy explosives TATB and FOX-7

Detection and differentiation of explosives is an issue of immediacy for security and environmental monitoring. At present, the most operated explosive system combines primary and secondary explosives. Although various advances are presently equipped for identifying primary explosives, the insensitive nature of secondary explosives makes their detection and differentiation challenging. Here, we report three coordination nanosheets (CNs) AM-8-Eu, AM-8-Tb, and AM-8-Zn containing three-way terpyridine ligands (AM-8) and Eu(III), Tb(III), and Zn(II) ions. These CNs act as a highly selective platform for detecting unexplored high-energy explosive 1,1-Diamino-2,2-dinitro ethylene (FOX-7) in solid and solution phases. For AM-8-Zn, calculated molecular electrostatic surface potential revealed two modes of interactions for triaminotrinitrobenzene (TATB) and FOX-7, producing two distinct spectral responses. Digital analysis of these responses revealed a complimentary implication (IMP)/not implication (NIMP) logic function. Finally, the unique feature of IMP gate is used to define all possible sixteen distinct binary Boolean operations on two logic values for “functional completeness”. The detection and differentiation of such insensitive high-energy explosives TATB and FOX-7, where Boolean logic can process information through distinct binary responses in solid and solution phases, have not been witnessed hitherto in a single chemical platform.

Optomagnetic Coordination Helical Robot with Shape Transformation and Multimodal Motion Capabilities.

Soft robots with magnetic responsiveness exhibit diverse motion modes and programmable shape transformations. While the fixed magnetization configuration facilitates coupling control of robot posture and motion, it limits individual posture control to some extent. This poses a challenge in independently controlling the robot's transformation and motion, restricting its versatile applications. This research introduces a multifunctional helical robot responsive to both light and magnetism, segregating posture control from movements. Light fields assist in robot shaping, achieving a 78% maximum diameter shift. Magnetic fields guide helical robots in multimodal motions, encompassing rotation, flipping, rolling, and spinning-induced propulsion. By controlling multimodal locomotion and shape transformation on demand, helical robots gain enhanced flexibility. This innovation allows them to tightly grip and wirelessly transport designated payloads, showcasing potential applications in drug delivery, soft grippers, and chemical reaction platforms. The unique combination of structural design and control methods holds promise for intelligent robots in the future.

A Protection Strategy for High‐yield Synthesis of Dimethyl Furan‐2,5‐dicarboxylate from 5‐Hydroxymethylfurfural Using Methanol as an Acetalizing Agent

AbstractDeveloping efficient catalytic processes for obtaining biobased monomers for plastics can significantly contribute to a more sustainable economy. The biomass‐derived and chemical platform 5‐hydroxymethylfurfural (HMF) can be converted to various key intermediates, which is often hampered by side reactions due to the reactive nature of the formyl group in HMF. Here, we present a stepwise approach involving a protecting agent to obtain dimethyl furan‐2,5‐dicarboxylate (MFDC), which is a monomer for polyalkylene furanoates, a new class of biobased polyesters. Methanol is used as a solvent, reactant, and protecting agent for HMF's formyl group. In the first step, the oxidative esterification of the hydroxymethyl group to methyl carboxylate in HMF‐dimethylacetal catalyzed by Au/CeO2 affords methyl 5‐formylfuran‐2‐carboxylate dimethylacetal (MFFC‐acetal) in high yields (>90 %) from concentrated methanolic solutions (~20 wt %). Without protecting agent, a mixture of methyl‐5‐hydroxymethylfuran‐2‐carboxylate (MHMFC), MFDC, and humin byproduct was obtained. The deprotection of MFFC‐acetal in the second step proceeded efficiently in acetone with Amberlyst‐15, affording MFFC in a >90 % yield. In the final step, oxidative esterification of MFFC in methanol (10 wt %) afforded MFDC in a 93 % yield using Au/CeO2. The acetal protection strategy with methanol offers an efficient route toward MFDC in two oxidative esterification steps.

A platform to induce and mature biomolecular condensates using chemicals and light.

Biomolecular condensates are membraneless compartments that impart spatial and temporal organization to cells. Condensates can undergo maturation, transitioning from dynamic liquid-like states into solid-like states associated with neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and Huntington's disease. Despite their important roles, many aspects of condensate biology remain incompletely understood, requiring tools for acutely manipulating condensate-relevant processes within cells. Here we used the BCL6 BTB domain and its ligands BI-3802 and BI-3812 to create a chemical genetic platform, BTBolig, allowing inducible condensate formation and dissolution. We also developed optogenetic and chemical methods for controlled induction of condensate maturation, where we surprisingly observed recruitment of chaperones into the condensate core and formation of dynamic biphasic condensates. Our work provides insights into the interaction of condensates with proteostasis pathways and introduces a suite of chemical-genetic approaches to probe the role of biomolecular condensates in health and disease.

Subcritical CO2–H2O hydrolysis of polyethylene terephthalate as a sustainable chemical recycling platform

Under optimized reaction conditions, CO2 can provide acidity to the reaction medium to favor waste poly(ethylene) terephthalate hydrolysis for subsequent recycling/upcycling.

Harnessing acetogenic bacteria for one-carbon valorization toward sustainable chemical production.

The pressing climate change issues have intensified the need for a rapid transition towards a bio-based circular carbon economy. Harnessing acetogenic bacteria as biocatalysts to convert C1 compounds such as CO2, CO, formate, or methanol into value-added multicarbon chemicals is a promising solution for both carbon capture and utilization, enabling sustainable and green chemical production. Recent advances in the metabolic engineering of acetogens have expanded the range of commodity chemicals and biofuels produced from C1 compounds. However, producing energy-demanding high-value chemicals on an industrial scale from C1 substrates remains challenging because of the inherent energetic limitations of acetogenic bacteria. Therefore, overcoming this hurdle is necessary to scale up the acetogenic C1 conversion process and realize a circular carbon economy. This review overviews the acetogenic bacteria and their potential as sustainable and green chemical production platforms. Recent efforts to address these challenges have focused on enhancing the ATP and redox availability of acetogens to improve their energetics and conversion performances. Furthermore, promising technologies that leverage low-cost, sustainable energy sources such as electricity and light are discussed to improve the sustainability of the overall process. Finally, we review emerging technologies that accelerate the development of high-performance acetogenic bacteria suitable for industrial-scale production and address the economic sustainability of acetogenic C1 conversion. Overall, harnessing acetogenic bacteria for C1 valorization offers a promising route toward sustainable and green chemical production, aligning with the circular economy concept.

Continuous flow synthesis enabling reaction discovery.

This article defines the role that continuous flow chemistry can have in new reaction discovery, thereby creating molecular assembly opportunities beyond our current capabilities. Most notably the focus is based upon photochemical, electrochemical and temperature sensitive processes where continuous flow methods and machine assisted processing can have significant impact on chemical reactivity patterns. These flow chemical platforms are ideally placed to exploit future innovation in data acquisition, feed-back and control through artificial intelligence (AI) and machine learning (ML) techniques.