Optimization of Tubulysin Antibody-Drug Conjugates: A Case Study in Addressing ADC Metabolism.

As part of our efforts to develop new classes of tubulin inhibitor payloads for antibody-drug conjugate (ADC) programs, we developed a tubulysin ADC that demonstrated excellent in vitro activity but suffered from rapid metabolism of a critical acetate ester. A two-pronged strategy was employed to address this metabolism. First, the hydrolytically labile ester was replaced by a carbamate functional group resulting in a more stable ADC that retained potency in cellular assays. Second, site-specific conjugation was employed in order to design ADCs with reduced metabolic liabilities. Using the later approach, we were able to identify a conjugate at the 334C position of the heavy chain that resulted in an ADC with considerably reduced metabolism and improved efficacy. The examples discussed herein provide one of the clearest demonstrations to-date that site of conjugation can play a critical role in addressing metabolic and PK liabilities of an ADC. Moreover, a clear correlation was identified between the hydrophobicity of an ADC and its susceptibility to metabolic enzymes. Importantly, this study demonstrates that traditional medicinal chemistry strategies can be effectively applied to ADC programs.

Design, Synthesis, and Cytotoxic Evaluation of Novel Tubulysin Analogues as ADC Payloads.

The tubulysin class of natural products has attracted much attention from the medicinal chemistry community due to its potent cytotoxicity against a wide range of human cancer cell lines, including significant activity in multidrug-resistant carcinoma models. As a result of their potency, the tubulysins have become an important tool for use in targeted therapy, being widely pursued as payloads in the development of novel small molecule drug conjugates (SMDCs) and antibody-drug conjugates (ADCs). A structure-based and parallel medicinal chemistry approach was applied to the synthesis of novel tubulysin analogues. These efforts led to the discovery of a number of novel and potent cytotoxic tubulysin analogues, providing a framework for our simultaneous report, which highlights the discovery of tubulysin-based ADCs, including use of site-specific conjugation to address in vivo stability of the C-11 acetate functionality.

Discovery of cytotoxic dolastatin 10 analogues with N-terminal modifications.

Auristatins, synthetic analogues of the antineoplastic natural product Dolastatin 10, are ultrapotent cytotoxic microtubule inhibitors that are clinically used as payloads in antibody-drug conjugates (ADCs). The design and synthesis of several new auristatin analogues with N-terminal modifications that include amino acids with α,α-disubstituted carbon atoms are described, including the discovery of our lead auristatin, PF-06380101. This modification of the peptide structure is unprecedented and led to analogues with excellent potencies in tumor cell proliferation assays and differential ADME properties when compared to other synthetic auristatin analogues that are used in the preparation of ADCs. In addition, auristatin cocrystal structures with tubulin are being presented that allow for the detailed examination of their binding modes. A surprising finding is that all analyzed analogues have a cis-configuration at the Val-Dil amide bond in their functionally relevant tubulin bound state, whereas in solution this bond is exclusively in the trans-configuration. This remarkable observation shines light onto the preferred binding mode of auristatins and serves as a valuable tool for structure-based drug design.

New cytotoxic benzosuberene analogs. Synthesis, molecular modeling and biological evaluation.

In this Letter we describe the synthesis and biological evaluation of new benzosuberene analogs with structural modifications on the B-ring. The focus was initially to probe the chemical space around the B-ring C-8 position. This position was readily available for derivatization chemistry using our recently developed new synthesis for this compound class. Furthermore, we describe two new B-ring analogs, one containing a diene and the other a cyclic ether group. Both new analogs show excellent potencies in tumor cell proliferation assays. In addition, we describe molecular modeling studies that provide a binding rationale for reference compound 8 in the colchicine binding site using the known colchicine crystal structure. We also examine whether the cell based potency data obtained with selected new analogs are supported by modeling results.

A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space.

We present here a comprehensive analysis of proteases in the peptide substrate space and demonstrate its applicability for lead discovery. Aligned octapeptide substrates of 498 proteases taken from the MEROPS peptidase database were used for the in silico analysis. A multiple-category naïve Bayes model, trained on the two-dimensional chemical features of the substrates, was able to classify the substrates of 365 (73%) proteases and elucidate statistically significant chemical features for each of their specific substrate positions. The positional awareness of the method allows us to identify the most similar substrate positions between proteases. Our analysis reveals that proteases from different families, based on the traditional classification (aspartic, cysteine, serine, and metallo), could have substrates that differ at the cleavage site (P1-P1') but are similar away from it. Caspase-3 (cysteine protease) and granzyme B (serine protease) are previously known examples of cross-family neighbors identified by this method. To assess whether peptide substrate similarity between unrelated proteases could reliably translate into the discovery of low molecular weight synthetic inhibitors, a lead discovery strategy was tested on two other cross-family neighbors--namely cathepsin L2 and matrix metallo proteinase 9, and calpain 1 and pepsin A. For both these pairs, a naïve Bayes classifier model trained on inhibitors of one protease could successfully enrich those of its neighbor from a different family and vice versa, indicating that this approach could be prospectively applied to lead discovery for a novel protease target with no known synthetic inhibitors.

Plate-based diversity selection based on empirical HTS data to enhance the number of hits and their chemical diversity.

Typically, screening collections of pharmaceutical companies contain more than a million compounds today. However, for certain high-throughput screening (HTS) campaigns, constraints posed by the assay throughput and/or the reagent costs make it impractical to screen the entire deck. Therefore, it is desirable to effectively screen subsets of the collection based on a hypothesis or a diversity selection. How to select compound subsets is a subject of ongoing debate. The authors present an approach based on extended connectivity fingerprints to carry out diversity selection on a per plate basis (instead of a per compound basis). HTS data from 35 Novartis screens spanning 5 target classes were investigated to assess the performance of this approach. The analysis shows that selecting a fingerprint-diverse subset of 250K compounds, representing 20% of the screening deck, would have achieved significantly higher hit rates for 86% of the screens. This measure also outperforms the Murcko scaffold-based plate selection described previously, where only 49% of the screens showed similar improvements. Strikingly, the 2-fold improvement in average hit rates observed for 3 of 5 target classes in the data set indicates a target bias of the plate (and thus compound) selection method. Even though the diverse subset selection lacks any target hypothesis, its application shows significantly better results for some targets-namely, G-protein-coupled receptors, proteases, and protein-protein interactions-but not for kinase and pathway screens. The synthetic origin of the compounds in the diverse subset appears to influence the screening hit rates. Natural products were the most diverse compound class, with significantly higher hit rates compared to the compounds from the traditional synthetic and combinatorial libraries. These results offer empirical guidelines for plate-based diversity selection to enhance hit rates, based on target class and the library type being screened.

Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis.

We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.

Mapping adverse drug reactions in chemical space.

We present a novel method to better investigate adverse drug reactions in chemical space. By integrating data sources about adverse drug reactions of drugs with an established cheminformatics modeling method, we generate a data set that is then visualized with a systems biology tool. Thereby new insights into undesired drug effects are gained. In this work, we present a global analysis linking chemical features to adverse drug reactions.

How similar are similarity searching methods? A principal component analysis of molecular descriptor space.

Different molecular descriptors capture different aspects of molecular structures, but this effect has not yet been quantified systematically on a large scale. In this work, we calculate the similarity of 37 descriptors by repeatedly selecting query compounds and ranking the rest of the database. Euclidean distances between the rank-ordering of different descriptors are calculated to determine descriptor (as opposed to compound) similarity, followed by PCA for visualization. Four broad descriptor classes are identified, which are circular fingerprints; circular fingerprints considering counts; path-based and keyed fingerprints; and pharmacophoric descriptors. Descriptor behavior is much more defined by those four classes than the particular parametrization. Using counts instead of the presence/absence of fingerprints significantly changes descriptor behavior, which is crucial for performance of topological autocorrelation vectors, but not circular fingerprints. Four-point pharmacophores (piDAPH4) surprisingly lead to much higher retrieval rates than three-point pharmacophores (28.21% vs 19.15%) but still similar rank-ordering of compounds (retrieval of similar actives). Looking into individual rankings, circular fingerprints seem more appropriate than path-based fingerprints if complex ring systems or branching patterns are present; count-based fingerprints could be more suitable in databases with a large number of repeated subunits (amide bonds, sugar rings, terpenes). Information-based selection of diverse fingerprints for consensus scoring (ECFP4/TGD fingerprints) led only to marginal improvement over single fingerprint results. While it seems to be nontrivial to exploit orthogonal descriptor behavior to improve retrieval rates in consensus virtual screening, those descriptors still each retrieve different actives which corroborates the strategy of employing diverse descriptors individually in prospective virtual screening settings.

The structure of urease activation complexes examined by flexibility analysis, mutagenesis, and small-angle X-ray scattering.

Conformational changes of Klebsiella aerogenes urease apoprotein (UreABC)(3) induced upon binding of the UreD and UreF accessory proteins were examined by a combination of flexibility analysis, mutagenesis, and small-angle X-ray scattering (SAXS). ProFlex analysis of urease provided evidence that the major domain of UreB can move in a hinge-like motion to account for prior chemical cross-linking results. Rigidification of the UreB hinge region, accomplished through a G11P mutation, reduced the extent of urease activation, in part by decreasing the nickel content of the mutant enzyme, and by sequestering a portion of the urease apoprotein in a novel activation complex that includes all of the accessory proteins. SAXS analyses of urease, (UreABC-UreD)(3), and (UreABC-UreDF)(3) confirm that UreD and UreF bind near UreB at the periphery of the (UreAC)(3) structure. This study supports an activation model in which a domain-shifted UreB conformation in (UreABC-UreDF)(3) allows CO(2) and nickel ions to gain access to the nascent active site.

"Virtual fragment linking": an approach to identify potent binders from low affinity fragment hits.

In this work we explore the possibilities of using fragment-based screening data to prioritize compounds from a full HTS library, a method we call virtual fragment linking (VFL). The ability of VFL to identify compounds of nanomolar potency based on micromolar fragment binding data was tested on 75 target classes from the WOMBAT database and succeeded in 57 cases. Further, the method was demonstrated for seven drug targets from in-house screening programs that performed both FBS of 8800 fragments and screens of the full library. VFL captured between 28% and 67% of the hits (IC 50 < 10microM) in the top 5% of the ranked library for four of the targets (enrichment between 5-fold and 13-fold). Our findings lead us to conclude that proper coverage of chemical space by the fragment library is crucial for the VFL methodology to be successful in prioritizing HTS libraries from fragment-based screening data.

Discovering new classes of Brugia malayi asparaginyl-tRNA synthetase inhibitors and relating specificity to conformational change.

SLIDE software, which models the flexibility of protein and ligand side chains while docking, was used to screen several large databases to identify inhibitors of Brugia malayi asparaginyl-tRNA synthetase (AsnRS), a target for anti-parasitic drug design. Seven classes of compounds identified by SLIDE were confirmed as micromolar inhibitors of the enzyme. Analogs of one of these classes of inhibitors, the long side-chain variolins, cannot bind to the adenosyl pocket of the closed conformation of AsnRS due to steric clashes, though the short side-chain variolins identified by SLIDE apparently bind isosterically with adenosine. We hypothesized that an open conformation of the motif 2 loop also permits the long side-chain variolins to bind in the adenosine pocket and that their selectivity for Brugia relative to human AsnRS can be explained by differences in the sequence and conformation of this loop. Loop flexibility sampling using Rigidity Optimized Conformational Kinetics (ROCK) confirms this possibility, while scoring of the relative affinities of the different ligands by SLIDE correlates well with the compounds' ranks in inhibition assays. Combining ROCK and SLIDE provides a promising approach for exploiting conformational flexibility in structure-based screening and design of species selective inhibitors.