Selecting Approaches for Hit Identification and Increasing Options by Building the Efficient Discovery of Actionable Chemical Matter from DNA-Encoded Libraries

High-Throughput DNA-Encoded Libraries Affinity Selection Platform for Binder Identification with Solid Support Protein Immobilization

DNA-encoded libraries (DELs) have demonstrated to be one of the most powerful technologies within the ligand identification toolbox, widely used either in academia or biotech and pharma companies. DEL methodology utilizes affinity selection (AS) as the approach to interrogate the protein of interest for the identification of binders. Here we present a high-throughput, fully automated AS platform developed to fulfill industrial standards and compatible with different assay formats to improve the reproducibility of the AS process for DEL binders identification. This platform is flexible enough to virtually set aside all kinds of DELs and AS methods and conditions using immobilized proteins. It bears the two main immobilization methods to support of the proteins of interest: magnetic beads or resin tip columns. A combination of a broad variety of protocol options with a wide range of different experimental conditions can be set up with a throughput of 96 samples at the same time. In addition, small modifications of the protocols provide the platform with the versatility to run not only the routine DEL screens, but also test covalent libraries, the successful immobilization of the proteins of interest, and many other experiments that may be required. This versatile AS platform for DEL can be a powerful instrument for direct application of the technology in academic and industry settings.

Affinity selection mass spectrometry speeding drug discovery

Affinity selection mass spectrometry (AS-MS) has gained momentum in drug discovery. This review summarizes how this technology has slowly risen as a new paradigm in hit identification and its potential synergy with DNA encoded library technology. It presents an overview of the recent results on challenging targets and perspectives on new areas of research, such as RNA targeting with small molecules. The versatility of the approach is illustrated and strategic drivers discussed in terms of the experience of a small-medium CRO and a big pharma organization.

Dose-Response Activity-Based DNA-Encoded Library Screening

Dose-response, or "conforming" behavior, increases confidence in a screening hit's authenticity. Here, we demonstrate dose-response solid-phase DNA-encoded library (DEL) screening. Compound dose in microfluidic droplets is modulated via the UV intensity of photocleavage from DEL beads. A 55,296-member DEL was screened at different UV intensities against model enzyme drug targets factor Xa (FXa) and autotaxin (ATX). Both screens yielded photochemical dose-dependent hit rates (FXa hit rates of 0.08/0.05% at 100/30% UV exposure; ATX hit rates of 0.24/0.08% at 100/20% UV exposure). FXa hits contained structures reflective of FXa inhibitors and four hits inhibited FXa (IC50 = 4.2 ± 0.1, 7.4 ± 0.3, 9.0 ± 0.3, and 19 ± 2 μM.) The top ATX hits (two dihydrobenzamidazolones and a tetrahydroisoquinoline) were validated as inhibitors (IC50 = 7 ± 2, 13 ± 2, and 1 ± 0.3 μM). Photochemical dose-response DEL screening data prioritized hits for synthesis, the rate-limiting step in DEL lead identification.

Machine-Learning-Based Data Analysis Method for Cell-Based Selection of DNA-Encoded Libraries

DNA-encoded library (DEL) is a powerful ligand discovery technology that has been widely adopted in the pharmaceutical industry. DEL selections are typically performed with a purified protein target immobilized on a matrix or in solution phase. Recently, DELs have also been used to interrogate the targets in the complex biological environment, such as membrane proteins on live cells. However, due to the complex landscape of the cell surface, the selection inevitably involves significant nonspecific interactions, and the selection data are much noisier than the ones with purified proteins, making reliable hit identification highly challenging. Researchers have developed several approaches to denoise DEL datasets, but it remains unclear whether they are suitable for cell-based DEL selections. Here, we report the proof-of-principle of a new machine-learning (ML)-based approach to process cell-based DEL selection datasets by using a Maximum A Posteriori (MAP) estimation loss function, a probabilistic framework that can account for and quantify uncertainties of noisy data. We applied the approach to a DEL selection dataset, where a library of 7,721,415 compounds was selected against a purified carbonic anhydrase 2 (CA-2) and a cell line expressing the membrane protein carbonic anhydrase 12 (CA-12). The extended-connectivity fingerprint (ECFP)-based regression model using the MAP loss function was able to identify true binders and also reliable structure-activity relationship (SAR) from the noisy cell-based selection datasets. In addition, the regularized enrichment metric (known as MAP enrichment) could also be calculated directly without involving the specific machine-learning model, effectively suppressing low-confidence outliers and enhancing the signal-to-noise ratio. Future applications of this method will focus on de novo ligand discovery from cell-based DEL selections.

Development of the Inverse Sonogashira Reaction for DEL Synthesis

An efficient approach for aryl acetylene DNA-encoded library (DEL) synthesis was developed in this study by transition-metal-mediated inverse Sonogashira reaction of 1-iodoalkyne with boronic acid under ambient conditions, with moderate to excellent conversions and broad substrate adaptability for the first time. Compared to palladium-phosphine, copper iodide performed better in the on-DNA inverse Sonogashira reaction. Interestingly, substrate diversity can be enhanced by first interrogating coupling reagents under copper-promoted conditions, and then revalidating them under palladium-facilitated conditions for those reagents which failed under the former. This complementary validation strategy is particularly well-fitted to any DEL validation studies.

Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function

DNA-encoded library (DEL) screening and quantitative structure-activity relationship (QSAR) modeling are two techniques used in drug discovery to find novel small molecules that bind a protein target. Applying QSAR modeling to DEL selection data can facilitate the selection of compounds for off-DNA synthesis and evaluation. Such a combined approach has been done recently by training binary classifiers to learn DEL enrichments of aggregated "disynthons" in order to accommodate the sparse and noisy nature of DEL data. However, a binary classification model cannot distinguish between different levels of enrichment, and information is potentially lost during disynthon aggregation. Here, we demonstrate a regression approach to learning DEL enrichments of individual molecules, using a custom negative-log-likelihood loss function that effectively denoises DEL data and introduces opportunities for visualization of learned structure-activity relationships. Our approach explicitly models the Poisson statistics of the sequencing process used in the DEL experimental workflow under a frequentist view. We illustrate this approach on a DEL dataset of 108,528 compounds screened against carbonic anhydrase (CAIX), and a dataset of 5,655,000 compounds screened against soluble epoxide hydrolase (sEH) and SIRT2. Due to the treatment of uncertainty in the data through the negative-log-likelihood loss used during training, the models can ignore low-confidence outliers. While our approach does not demonstrate a benefit for extrapolation to novel structures, we expect our denoising and visualization pipeline to be useful in identifying structure-activity trends and highly enriched pharmacophores in DEL data. Further, this approach to uncertainty-aware regression modeling is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.

Small-Molecule Lead-Finding Trends across the Roche and Genentech Research Organizations

The origin of small-molecule leads that were pursued across the independent research organizations Roche and Genentech from 2009 to 2020 is described. The identified chemical series are derived from a variety of lead-finding methods, which include public information, high-throughput screening (both full file and focused), fragment-based design, DNA-encoded library technology, use of legacy internal data, in-licensing, and de novo design (often structure-based). The translation of the lead series into in vivo tool compounds and development candidates is discussed as are the associated biological target classes and corresponding therapeutic areas. These analyses identify important trends regarding the various lead-finding approaches, which will likely impact their future application in the Roche and Genentech research groups. They also highlight commonalities and differences across the two independent research organizations. Several caveats associated with the employed data collection and analysis methodologies are included to enhance the interpretation of the presented information.

Understanding Data Noise and Uncertainty through Analysis of Replicate Samples in DNA-Encoded Library Selection

By analyzing data sets of replicate DNA-Encoded Library (DEL) selections, an approach for estimating the noise level of the experiment has been developed. Using a logarithm transformation of the number of counts associated with each compound and a subset of compounds with the highest number of counts, it is possible to assess the quality of the data through normalizing the replicates and use this same data to estimate the noise in the experiment. The noise level is seen to be dependent on sequencing depth as well as specific selection conditions. The noise estimation is independent of any cutoff used to remove low frequency compounds from the data analysis. The removal of compounds with only 1-5 read counts greatly reduces some of the challenges encountered in DEL data analysis as it can reduce the data set by greater than 100-fold without impacting the interpretation of the results.

Discovery of a Highly Selective BET BD2 Inhibitor from a DNA-Encoded Library Technology Screening Hit

Second-generation bromodomain and extra terminal (BET) inhibitors, which selectively target one of the two bromodomains in the BET proteins, have begun to emerge in the literature. These inhibitors aim to help determine the roles and functions of each domain and assess whether they can demonstrate an improved safety profile in clinical settings compared to pan-BET inhibitors. Herein, we describe the discovery of a novel BET BD2-selective chemotype using a structure-based drug design from a hit identified by DNA-encoded library technologies, showing a structural differentiation from key previously reported greater than 100-fold BD2-selective chemotypes GSK620, GSK046, and ABBV-744. Following a structure-based hypothesis for the selectivity and optimization of the physicochemical properties of the series, we identified 60 (GSK040), an in vitro ready and in vivo capable BET BD2-inhibitor of unprecedented selectivity (5000-fold) against BET BD1, excellent selectivity against other bromodomains, and good physicochemical properties. This novel chemical probe can be added to the toolbox used in the advancement of epigenetics research.

DNA-Encoded Library Hit Confirmation: Bridging the Gap Between On-DNA and Off-DNA Chemistry

DNA-encoded library (DEL) technology is a powerful platform for hit identification in academia and the pharmaceutical industry. When conducting off-DNA resynthesis hit confirmation after affinity selection, PCR/sequencing, and data analysis, one typically assumes a "one-to-one" relationship between the DNA tag and the chemical structure of the attached small-molecule it encodes. Because library synthesis often yields a mixture, this approximation increases the risk of overlooking positive discoveries and valuable information. To address this issue, we apply a library synthesis "recipe" strategy for on-DNA resynthesis using a cleavable linker, followed by direct affinity selection mass spectrometry (AS-MS) evaluation and identification of binder(s) from the released small-molecule mixture. We validate and showcase this approach employing the receptor-interacting-protein kinase 2 (RIP2) DEL campaign. We also designed and developed two cleavable linkers to enable this method, a photocleavable linker (nitrophenyl-based) and acid-labile linker (tetrahydropyranyl ether). The strategy provides an effective means of hit identification and rapid determination of key active component(s) of the mixture.

Toward the assembly and characterization of an encoded library hit confirmation platform: Bead-Assisted Ligand Isolation Mass Spectrometry (BALI-MS)

The ability to predict chemical structure from DNA sequence has to date been a necessary cornerstone of DNA-encoded library technology. DNA-encoded libraries (DELs) are typically screened by immobilized affinity selection and enriched library members are identified by counting the number of times an individual compound's sequence is observed in the resultant dataset. Those with high signal reads (DEL hits) are subsequently followed up through off-DNA synthesis of the predicted small molecule structures. However, hits followed-up in this manner often fail to translate to confirmed ligands. To address this low conversion rate of DEL hits to off-DNA ligands, we have developed an approach that eliminates the reliance on chemical structure prediction from DNA sequence. Here we describe our method of combining non-combinatorial resynthesis on-DNA following library procedures as a rapid means to assess the probable molecules attached to the DNA barcode. Furthermore, we apply our Bead-Assisted Ligand Isolation Mass Spectrometry (BALI-MS) technique to identify the true binders found within the mixtures of on-DNA synthesis products. Finally, we describe a Normalized Enrichment (NE) metric that allows for the quantitative assessment of affinity selection in these studies. We exemplify how this combined approach enables the identification of putative hit matter against a clinically relevant therapeutic target bisphosphoglycerate mutase, BPGM.

Recent Advances on the Selection Methods of DNA-Encoded Libraries

DNA-encoded libraries (DEL) have come of age and become a major technology platform for ligand discovery in both academia and the pharmaceutical industry. Technological maturation in the past two decades and the recent explosive developments of DEL-compatible chemistries have greatly improved the chemical diversity of DELs and fueled its applications in drug discovery. A relatively less-covered aspect of DELs is the selection method. Typically, DEL selection is considered as a binding assay and the selection is conducted with purified protein targets immobilized on a matrix, and the binders are separated from the non-binding background via physical washes. However, the recent innovations in DEL selection methods have not only expanded the target scope of DELs, but also revealed the potential of the DEL technology as a powerful tool in exploring fundamental biology. In this Review, we first cover the "classic" DEL selection methods with purified proteins on solid phase, and then we discuss the strategies to realize DEL selections in solution phase. Finally, we focus on the emerging approaches for DELs to interrogate complex biological targets.

Triaging of DNA-Encoded Library Selection Results by High-Throughput Resynthesis of DNA-Conjugate and Affinity Selection Mass Spectrometry

DNA encoded library (DEL) technology allows for rapid identification of novel small-molecule ligands and thus enables early-stage drug discovery. DEL technology is well-established, numerous cases of discovered hit molecules have been published, and the technology is widely employed throughout the pharmaceutical industry. Nonetheless, DEL selection results can be difficult to interpret, as library member enrichment may derive from not only desired products, but also DNA-conjugated byproducts and starting materials. Note that DELs are generally produced using split-and-pool combinatorial chemistry, and DNA-conjugated byproducts and starting materials cannot be removed from the library mixture. Herein, we describe a method for high-throughput parallel resynthesis of DNA-conjugated molecules such that byproducts, starting materials, and desired products are produced in a single pot, using the same chemical reactions and reagents as during library production. The low-complexity mixtures of DNA-conjugate are then assessed for protein binding by affinity selection mass spectrometry and the molecular weights of the binding ligands ascertained. This workflow is demonstrated to be a practical tool to triage and validate potential hits from DEL selection data.