High-Throughput and Format-Agnostic Mispairing Assay for Multispecific Antibodies Using Intact Mass Spectrometry.

Multispecific antibodies have gained significant importance in a broad indication space due to their ability to engage multiple epitopes simultaneously and to thereby overcome therapeutic barriers. With growing therapeutic potential, however, the molecular complexity increases, thus intensifying the demand for innovative protein engineering and analytical strategies. A major challenge for multispecific antibodies is the correct assembly of light and heavy chains. Engineering strategies exist to stabilize the correct pairing, but typically individual engineering campaigns are required to arrive at the anticipated format. Mass spectrometry has proven to be a versatile tool to identify mispaired species. However, due to manual data analysis procedures, mass spectrometry is limited to lower throughputs. To keep pace with increasing sample numbers, we developed a high-throughput-capable mispairing workflow based on intact mass spectrometry with automated data analysis, peak detection, and relative quantification using Genedata Expressionist. This workflow is capable of detecting mispaired species of ∼1000 multispecific antibodies in three weeks and thus is applicable to complex screening campaigns. As a proof of concept, the assay was applied to engineering a trispecific antibody. Strikingly, the new setup has not only proved successful in mispairing analysis but has also revealed its potential to automatically annotate other product-related impurities. Furthermore, we could confirm the assay to be format-agnostic, as shown by analyzing several different multispecific formats in one run. With these comprehensive capabilities, the new automated intact mass workflow can be applied as a universal tool to detect and annotate peaks in a format-agnostic approach and in high-throughput, thus enabling complex discovery campaigns.

Is scaffold hopping a reliable indicator for the ability of computational methods to identify structurally diverse active compounds?

Computational scaffold hopping aims to identify core structure replacements in active compounds. To evaluate scaffold hopping potential from a principal point of view, regardless of the computational methods that are applied, a global analysis of conventional scaffolds in analog series from compound activity classes was carried out. The majority of analog series was found to contain multiple scaffolds, thus enabling the detection of intra-series scaffold hops among closely related compounds. More than 1000 activity classes were found to contain increasing proportions of multi-scaffold analog series. Thus, using such activity classes for scaffold hopping analysis is likely to overestimate the scaffold hopping (core structure replacement) potential of computational methods, due to an abundance of artificial scaffold hops that are possible within analog series.

Assessing Scaffold Diversity of Kinase Inhibitors Using Alternative Scaffold Concepts and Estimating the Scaffold Hopping Potential for Different Kinases.

Publicly available kinase inhibitors provide a large source of information for structure-activity relationship analysis and kinase drug design. In this study, publicly available inhibitors of the human kinome were collected and analog series formed by kinase inhibitors systematically identified. Then, alternative scaffold concepts were applied to assess diversity and promiscuity of kinase inhibitors. Over the past two years, the number of publicly available kinase inhibitors with high-confidence activity data more than doubled, but coverage of the human kinome only slightly increased. Approximately 70% of current kinase inhibitors belonged to analog series. However, the detectable degree of promiscuity among these kinase inhibitors remained low. Approximately 76% of all inhibitors were only annotated with a single kinase, compared to ~70% two years ago. For many kinases, the assessment of scaffold diversity among their inhibitors and the distribution of differently defined scaffolds over analog series made it possible to assess scaffold hopping potential. Our analysis revealed that the consideration of conventional compound-based scaffolds most likely leads to an overestimation of scaffold hopping frequency, at least for compounds forming analog series.

One hundred ABO-incompatible kidney transplantations between 2004 and 2014: a single-centre experience.

BACKGROUND: ABO-incompatible kidney transplantation (ABOi KTx) expands the living donor transplantation options. However, long-term outcome data, especially in comparison with ABO-compatible kidney transplantation (ABOc KTx), remain limited. Since the first ABOi KTx in Germany on 1 April 2004 at our centre, we have followed 100 ABOi KTx over up to 10 years. METHODS: One hundred ABOi KTx and 248 ABOc KTx from 1 April 2004 until 28 October 2014 were analysed in this observational, single-centre study. Three ABOi KTx and 141 ABOc KTx were excluded because of cyclosporine A-based immunosuppression, and 1 ABOc KTx was lost to follow-up. RESULTS: Median estimated 10-year patient and graft survival in ABOi KTx was 99 and 94%, respectively, and surpassed ABOc-KTx patient and graft survival of 80 and 88%, respectively. The incidence rate of antibody-mediated rejections was 10 and 8%, and that of T-cell-mediated rejections was 17 and 20% in ABOi KTx and ABOc KTx, respectively. Infectious and malignant complications in ABOi KTx were not more common than in ABOc KTx. However, postoperative lymphoceles occurred more frequently in ABOi KTx. Subgroup analysis of ABOi-KTx patients revealed that patients with high-titre isohaemagglutinins before transplantation had equal long-term results compared with low-titre isohaemagglutinin patients. CONCLUSION: Taken together, long-term outcome of ABOi KTx is not inferior to ABOc KTx. Incidences of rejection episodes, infectious complications and malignancies are not increased, despite the more vigorous immunosuppression in ABOi KTx. Our data provide further evidence that ABOi KTx with living donation is a safe, successful and reasonable option to reduce the organ shortage.

Visualization of multi-property landscapes for compound selection and optimization.

Compound optimization generally requires considering multiple properties in concert and reaching a balance between them. Computationally, this process can be supported by multi-objective optimization methods that produce numerical solutions to an optimization task. Since a variety of comparable multi-property solutions are usually obtained further prioritization is required. However, the underlying multi-dimensional property spaces are typically complex and difficult to rationalize. Herein, an approach is introduced to visualize multi-property landscapes by adapting the concepts of star and parallel coordinates from computer graphics. The visualization method is designed to complement multi-objective compound optimization. We show that visualization makes it possible to further distinguish between numerically equivalent optimization solutions and helps to select drug-like compounds from multi-dimensional property spaces. The methodology is intuitive, applicable to a wide range of chemical optimization problems, and made freely available to the scientific community.

Systematic assessment of scaffold hopping versus activity cliff formation across bioactive compound classes following a molecular hierarchy.

Scaffold hopping and activity cliff formation define opposite ends of the activity landscape feature spectrum. To rationalize these events at the level of scaffolds, active compounds involved in scaffold hopping were required to contain topologically distinct scaffolds but have only limited differences in potency, whereas compounds involved in activity cliffs were required to share the same scaffold but have large differences in potency. A systematic search was carried out for compounds involved in scaffold hopping and/or activity cliff formation. Results obtained for compound data sets covering more than 300 human targets revealed clear trends. If scaffolds represented multiple but fewer than 10 active compounds, nearly 90% of all scaffolds were exclusively involved in hopping events. With increasing compound coverage, the fraction of scaffolds involved in both scaffold hopping and activity cliff formation significantly increased to more than 50%. However, ∼40% of the scaffolds representing large numbers of active compounds continued to be exclusively involved in scaffold hopping. More than 200 scaffolds with broad target coverage were identified that consistently represented potent compounds and yielded an abundance of scaffold hops in the low-nanomolar range. These and other subsets of scaffolds we characterized are of prime interest for structure-activity relationship (SAR) exploration and compound design. Therefore, the complete scaffold classification generated in the course of our analysis is made freely available.

Composition and topology of activity cliff clusters formed by bioactive compounds.

The assessment of activity cliffs has thus far mostly focused on compound pairs, although the majority of activity cliffs are not formed in isolation but in a coordinated manner involving multiple active compounds and cliffs. However, the composition of coordinated activity cliff configurations and their topologies are unknown. Therefore, we have identified all activity cliff configurations formed by currently available bioactive compounds and analyzed them in network representations where activity cliff configurations occur as clusters. The composition, topology, frequency of occurrence, and target distribution of activity cliff clusters have been determined. A limited number of large cliff clusters with unique topologies were identified that were centers of activity cliff formation. These clusters originated from a small number of target sets. However, most clusters were of small to moderate size. Three basic topologies were sufficient to describe recurrent activity cliff cluster motifs/topologies. For example, frequently occurring clusters with star topology determined the scale-free character of the global activity cliff network and represented a characteristic activity cliff configuration. Large clusters with complex topology were often found to contain different combinations of basic topologies. Our study provides a first view of activity cliff configurations formed by currently available bioactive compounds and of the recurrent topologies of activity cliff clusters. Activity cliff clusters of defined topology can be selected, and from compounds forming the clusters, SAR information can be obtained. The SAR information of activity cliff clusters sharing a/one specific activity and topology can be compared.

Large-scale assessment of activity landscape feature probabilities of bioactive compounds.

Activity landscape representations integrate pairwise compound similarity and potency relationships and provide direct access to characteristic structure-activity relationship features in compound data sets. Because pairwise compound comparisons provide the foundation of activity landscape design, the assessment of specific landscape features such as activity cliffs has generally been confined to the level of compound pairs. A conditional probability-based approach has been applied herein to assign most probable activity landscape features to individual compounds. For example, for a given data set compound, it was determined if it would preferentially engage in the formation of activity cliffs or other landscape features. In a large-scale effort, we have determined conditional activity landscape feature probabilities for more than 160,000 compounds with well-defined activity annotations contained in 427 different target-based data sets. These landscape feature probabilities provide a detailed view of how different activity landscape features are distributed over currently available bioactive compounds.

Quantifying the fingerprint descriptor dependence of structure-activity relationship information on a large scale.

It is well-known that different molecular representations, e.g., graphs, numerical descriptors, fingerprints, or 3D models, change the numerical results of molecular similarity calculations. Because the assessment of structure-activity relationships (SARs) requires similarity and potency comparisons of active compounds, this representation dependence inevitably also affects SAR analysis. But to what extent? How exactly does SAR information change when alternative fingerprints are used as descriptors? What is the proportion of active compounds with substantial changes in SAR information induced by different fingerprints? To provide answers to these questions, we have quantified changes in SAR information across many different compound classes using six different fingerprints. SAR profiling was carried out on 128 target-based data sets comprising more than 60,000 compounds with high-confidence activity annotations. A numerical measure of SAR discontinuity was applied to assess SAR information on a per compound basis. For ~70% of all test compounds, changes in SAR characteristics were detected when different fingerprints were used as molecular representations. Moreover, the SAR phenotype of ~30% of the compounds changed, and distinct fingerprint-dependent local SAR environments were detected. The fingerprints we compared were found to generate SAR models that were essentially not comparable. Atom environment and pharmacophore fingerprints produced the largest differences in compound-associated SAR information. Taken together, the results of our systematic analysis reveal larger fingerprint-dependent changes in compound-associated SAR information than would have been anticipated.

Compound pathway model to capture SAR progression: comparison of activity cliff-dependent and -independent pathways.

A compound pathway model is introduced to monitor SAR progression in compound data sets. Pathways are formed by sequences of structurally analogous compounds with stepwise increasing potency that ultimately yield highly potent compounds. Hence, the model was designed to mimic compound optimization efforts. Different pathway categories were defined. Pathways originating from any active compound in a data set were systematically identified including compounds forming activity cliffs. The relative frequency of activity cliff-dependent and -independent pathways was determined and compared. In 23 of 39 different compound data sets that qualified for our analysis, significant differences in the relative frequency of activity cliff-dependent and -independent pathways were observed. In 17 of these 23 data sets, activity cliff-dependent pathways occurred with higher relative frequency than cliff-independent pathways. In addition, pathways originating from the majority of activity cliff compounds displayed desired SAR progression, reflecting SAR information gain associated with activity cliffs.

Navigating high-dimensional activity landscapes: design and application of the ligand-target differentiation map.

The transformation of high-dimensional bioactivity spaces into activity landscape representations is as of yet an unsolved problem in computational medicinal chemistry. High-dimensional activity spaces result from the experimental evaluation of compound sets on large numbers of targets. We introduce a first concept to represent and navigate high-dimensional activity landscapes that is based on a data structure termed ligand-target differentiation (LTD) map. This approach is designed to reduce the complexity of high-dimensional bioactivity spaces and enable the identification and further analysis of compound subsets with interesting activity and structural relationships. Its utility has been demonstrated using a set of more than 1400 inhibitors with exact activity measurements for varying numbers of 172 kinases.

Design of multitarget activity landscapes that capture hierarchical activity cliff distributions.

An activity landscape model of a compound data set can be rationalized as a graphical representation that integrates molecular similarity and potency relationships. Activity landscape representations of different design are utilized to aid in the analysis of structure-activity relationships and the selection of informative compounds. Activity landscape models reported thus far focus on a single target (i.e., a single biological activity) or at most two targets, giving rise to selectivity landscapes. For compounds active against more than two targets, landscapes representing multitarget activities are difficult to conceptualize and have not yet been reported. Herein, we present a first activity landscape design that integrates compound potency relationships across multiple targets in a formally consistent manner. These multitarget activity landscapes are based on a general activity cliff classification scheme and are visualized in graph representations, where activity cliffs are represented as edges. Furthermore, the contributions of individual compounds to structure-activity relationship discontinuity across multiple targets are monitored. The methodology has been applied to derive multitarget activity landscapes for compound data sets active against different target families. The resulting landscapes identify single-, dual-, and triple-target activity cliffs and reveal the presence of hierarchical cliff distributions. From these multitarget activity landscapes, compounds forming complex activity cliffs can be readily selected.

Trace elements in commonly used medicinal plants from Varna region, Bulgaria.

In this study, we assessed the concentrations of some trace and toxic elements (Cd, Pb, Zn, Cu, Ni, Cr, Fe, and Mn) in traditionally used wild medicinal plants: chamomile (Matricaria chamomilla), white yarrow (Achillea millefolium), linden (Tilia sp.), and elder (Sambucus nigra L.) collected from urban and rural region near Varna, Bulgaria. Concentrations of examined elements were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES) after sample mineralization. The samples were digested with concentrated nitric acid in a microwave system. Mean element levels in medicinal herbs were established in decreasing order as follows: Fe > Mn > Zn ≈ Cu > Pb > Ni > Cr > Cd. The content of toxic elements in medicinal herbs was found significantly higher in areas with intensive traffic compared with a suburban area. Instead discrimination between metal content in rural and urban samples was confirmed by PCA. Concentrations of toxic elements Cd and Pb in all plant species from the rural region were found in the range from not detected to 0.08 and 0.796 ng/kg, respectively and were below the permissible limit of the WHO.

An end-to-end automated platform process for high-throughput engineering of next-generation multi-specific antibody therapeutics.

Next-generation multi-specific antibody therapeutics (MSATs) are engineered to combine several functional activities into one molecule to provide higher efficacy compared to conventional, mono-specific antibody therapeutics. However, highly engineered MSATs frequently display poor yields and less favorable drug-like properties (DLPs), which can adversely affect their development. Systematic screening of a large panel of MSAT variants in very high throughput (HT) is thus critical to identify potent molecule candidates with good yield and DLPs early in the discovery process. Here we report on the establishment of a novel, format-agnostic platform process for the fast generation and multiparametric screening of tens of thousands of MSAT variants. To this end, we have introduced full automation across the entire value chain for MSAT engineering. Specifically, we have automated the in-silico design of very large MSAT panels such that it reflects precisely the wet-lab processes for MSAT DNA library generation. This includes mass saturation mutagenesis or bulk modular cloning technologies while, concomitantly, enabling library deconvolution approaches using HT Sanger DNA sequencing. These DNA workflows are tightly linked to fully automated downstream processes for compartmentalized mammalian cell transfection expression, and screening of multiple parameters. All sub-processes are seamlessly integrated with tailored workflow supporting bioinformatics. As described here, we used this platform to perform multifactor optimization of a next-generation bispecific, cross-over dual variable domain-Ig (CODV-Ig). Screening of more than 25,000 individual protein variants in mono- and bispecific format led to the identification of CODV-Ig variants with over 1,000-fold increased potency and significantly optimized production titers, demonstrating the power and versatility of the platform.

Mapping Biological Activities to Different Types of Molecular Scaffolds: Exemplary Application to Protein Kinase Inhibitors.

Scaffolds were originally introduced to delineate core structures of active compounds. They are also used to assess the ability of computational methods to identify structurally diverse active compounds. Biological activities of compound series are often mapped to scaffolds. This is done to better understand activity distributions over different structural classes or search for core structures of compounds that are preferentially active against target families of interest. Herein, we describe in detail how such scaffold activity profiles are generated and compare profiles for differently defined scaffolds. As an exemplary application, scaffolds of currently available kinase inhibitors covering the human kinome are analyzed.

Rationalizing Promiscuity Cliffs.

Compound promiscuity can be viewed in different ways. We distinguish "bad" promiscuity resulting from chemical liabilities, nonspecific binding, or assay artifacts, from "good" promiscuity representing true multitarget activities. Investigating multitarget activities of small molecules is scientifically stimulating and therapeutically relevant. To better understand the molecular basis of multitarget activities, structure-promiscuity relationships (SPRs) are explored. For this purpose, "promiscuity cliffs" (PCs) have been introduced, which can be rationalized as an extension of the activity cliff (AC) concept. A PC is defined as a pair of structural analogues that are active against different numbers of targets (given a difference threshold). As discussed herein PCs frequently capture surprising SPRs and encode many experimentally testable hypotheses.

Collection of analog series-based scaffolds from public compound sources.

AIM: Providing a large and freely available in silico collection of analog series-based (ASB) scaffolds for computational design and medicinal chemistry applications. METHODOLOGY: Using a recently introduced computational method, ASB scaffolds with single and multiple substitution sites were systematically isolated from publicly available active compounds. DATA: A total of 23,791 unique ASB scaffolds are made available in an organized and machine-readable form as an open access deposition. For each ASB scaffold, the number of analogs it represents is also provided. NEXT STEPS: The freely available collection of ASB scaffolds will be further analyzed to explore different types of scaffold relationships. The ASB scaffold collection will periodically be updated.

Computational design of new molecular scaffolds for medicinal chemistry, part II: generalization of analog series-based scaffolds.

AIM: Extending and generalizing the computational concept of analog series-based (ASB) scaffolds. MATERIALS & METHODS: Methodological modifications were introduced to further increase the coverage of analog series (ASs) and compounds by ASB scaffolds. From bioactive compounds, ASs were systematically extracted and second-generation ASB scaffolds isolated. RESULTS: More than 20,000 second-generation ASB scaffolds with single or multiple substitution sites were extracted from active compounds, achieving more than 90% coverage of ASs. CONCLUSION: Generalization of the ASB scaffold approach has yielded a large knowledge base of scaffold-capturing compound series and target information.

Redundancy in two major compound databases.

Public repositories of compounds and activity data are of prime importance for pharmaceutical research in academic and industrial settings. Major databases have evolved over the years. Their growth is accompanied by an increasing tendency toward data sharing. This is a positive development but not without potential problems. Using ChEMBL and PubChem as examples, we show that crosstalk between databases also leads to substantial data redundancy that might not be obvious. Redundancy is an important issue because it biases data analysis and knowledge extraction and leads to inflated views of available compounds, assays and activity data. Going forward it will be important to further refine data exchange and deposition criteria and make redundancy as transparent as possible.

Application of a New Scaffold Concept for Computational Target Deconvolution of Chemical Cancer Cell Line Screens.

Target deconvolution of phenotypic assays is a hot topic in chemical biology and drug discovery. The ultimate goal is the identification of targets for compounds that produce interesting phenotypic readouts. A variety of experimental and computational strategies have been devised to aid this process. A widely applied computational approach infers putative targets of new active molecules on the basis of their chemical similarity to compounds with activity against known targets. Herein, we introduce a molecular scaffold-based variant for similarity-based target deconvolution from chemical cancer cell line screens that were used as a model system for phenotypic assays. A new scaffold type was used for substructure-based similarity assessment, termed analog series-based (ASB) scaffold. Compared with conventional scaffolds and compound-based similarity calculations, target assignment centered on ASB scaffolds resulting from screening hits and bioactive reference compounds restricted the number of target hypotheses in a meaningful way and lead to a significant enrichment of known cancer targets among candidates.