Mario Lobell

Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits

Drug-like and lead-like hits derived from HTS campaigns provide good starting points for lead optimization. However, too strong emphasis on potency as hit-selection parameter might hamper the success of such projects. A detailed absorption, distribution, metabolism, excretion and toxicology (ADME-Tox) profiling is needed to help identify hits with a minimum number of (known) liabilities. This is particularly true for drug-like hits. Herein, we describe how to break down large numbers of screening hits and we provide a comprehensive overview of the strengths and weaknesses for each structural class. The overall profile (e.g. ligand efficiency, selectivity and ADME-Tox) is the distinctive feature that will define the priority for follow-up.

In Silico ADMET Traffic Lights as a Tool for the Prioritization of HTS Hits

The need for in silico characterization of HTS hit structures as part of a data‐driven hit‐selection process is demonstrated. A solution is described in the form of an in silico ADMET traffic light and PhysChem scoring system. This has been extensively validated with in‐house data at Bayer, published data, and a collection of launched small‐molecule oral drugs.

CypScore: Quantitative prediction of reactivity toward cytochromes P450 based on semiempirical molecular orbital theory

CypScore predicts the reactivity of competing positions in the same and different molecules to a variety of cytochrome P450 metabolic reactions on a single reactivity scale.

Cardiovascular effects of a novel potent and highly selective azaindole-based inhibitor of Rho-kinase

Rho‐kinase (ROCK) has been implicated in the pathophysiology of altered vasoregulation leading to hypertension. Here we describe the pharmacological characterization of a potent, highly selective and orally active ROCK inhibitor, the derivative of a class of azaindoles, azaindole 1(6‐chloro‐N 4‐{3,5‐difluoro‐4‐[(3‐methyl‐1H‐pyrrolo[2,3‐b]pyridin‐4‐yl)oxy]‐phenyl}pyrimidine‐2,4‐diamine).

Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pKa prediction.

In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of ∼14,000 literature pKa values (∼11,000 compounds, representing literature chemical space) and ∼19,500 pKa values experimentally determined at Bayer Pharma (∼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of ∼31,000 new pKa values measured at Bayer. For the largest and most difficult test set with >16,000 pKa values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (R(2)) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and R(2) = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new pKa prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists.

Design and Synthesis of Potent and Selective Azaindole‐Based Rho Kinase (ROCK) Inhibitors

Rho kinase plays a pivotal role in several cellular processes such as vasoregulation, making it a suitable target for the treatment of hypertension and related disorders. We discovered a new compound class of Rho kinase (ROCK) inhibitors containing a 7‐azaindole hinge‐binding scaffold tethered to an aminopyrimidine core. Herein we describe the structure–activity relationships elucidated through biochemical and functional assays. The introduction of suitable substituents at the 3‐position of the bicyclic moiety led to an increase in activity, which was required to design compounds with favorable pharmacokinetic profile. Azaindole 32 was identified as a highly selective and orally available ROCK inhibitor able to cause a sustained blood pressure reduction in vivo.

Abstract 3234: Development of potent and selective antibody-drug conjugates with pyrrole-based KSP inhibitors as novel payload class

The number of cytotoxic payload classes with different modes-of-action which have been successfully employed in antibody-drug conjugates (ADC) is still rather limited. So far, only ADCs with microtubule inhibitors, DNA binding payloads or topoisomerase I inhibitors have been advanced into clinical testing. To this end, the identification of ADC payload classes with a novel mode of action will increase therapeutic options and potentially help to overcome resistance. Inhibitors of kinesin spindle protein (KSP/Eg5) have generated interest due to their high antitumor potency. However, transferring the preclinical potency of small molecule KSP inhibitors (KSPis) into highly efficient clinical regimens with a sufficient therapeutic window has remained challenging. We have investigated a new pyrrole subclass of KSPis which showed subnanomolar potency against a large panel of tumor cell lines for their utility as a novel payload class in ADCs. Towards this goal different attachment sites for linkers have been explored in the KSPi molecule which were found compatible with cleavable and/or non-cleavable linkers. Subnanomolar potency and selectivity of ADCs with antibodies targeting either HER2, EGFR or TWEAKR could be demonstrated in vitro. For selected ADCs, the intracellular trafficking and metabolite formation was investigated and KSP inhibition was confirmed as the ADC mode of action. Depending on the linker composition differential profiles of the ADC metabolites with regard to efflux, cellular permeation, and bystander effect have been achieved. Moreover, specific accumulation in the tumor versus other tissues was demonstrated in biodistribution studies in vivo. In conclusion, KSP inhibitors have been established as a versatile new payload class for the generation of highly potent and selective ADCs. Citation Format: Hans-Georg Lerchen, Sven Wittrock, Nils Griebenow, Mario Lobell, Anne-Sophie Rebstock, Yolanda Cancho-Grande, Beatrix Stelte-Ludwig, Christoph Mahlert, Simone Greven, Anette Sommer, Sandra Berndt, Carsten Terjung, Heiner Apeler, Bertolt Kreft, Rolf Jautelat. Development of potent and selective antibody-drug conjugates with pyrrole-based KSP inhibitors as novel payload class [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3234. doi:10.1158/1538-7445.AM2017-3234

Abstract 4332: Discovery of BAY 1163877 - A pan-FGFR inhibitor: De novo structure-based design and lead optimization of benzothiophenyl-pyrrolotriazines

Fibroblast growth factors (FGFs) orchestrate a variety of cellular functions by binding to their transmembrane tyrosine-kinase receptors (FGFR1-4) and activating downstream signaling pathways. Alterations in FGFR encoding genes are frequently observed in a variety of solid tumors including lung, gastric, breast and urothelial cancer. Therefore, targeting FGFRs using selective FGFR inhibitors is an attractive therapeutic approach to treat cancer patients. BAY 1163877 is an orally active, highly potent and selective small molecule FGFR-1, -2 and -3 kinase inhibitor. We disclose for the very first time its discovery and chemical structure. BAY 1163877 was derived from a de novo structure-based design approach and medicinal chemistry optimization. Data on the structure activity relationship and the pharmacokinetic profile of the benzothiophenyl-pyrrolotriazine structure class will be presented. Based on its favorable preclinical profile, BAY 1163877 is currently being investigated in a Phase 1 clinical trial (NCT01976741). Citation Format: Marie-Pierre L. Collin, Mario Lobell, Walter Huebsch, Dirk Brohm, Melanie Heroult, Klemens Lustig, Sylvia Gruenewald, Ulf Boemer, Rolf Jautelat, Holger Hess-Stump, Stefan Jaroch, Michael Brands, Karl Ziegelbauer. Discovery of BAY 1163877 - A pan-FGFR inhibitor: De novo structure-based design and lead optimization of benzothiophenyl-pyrrolotriazines. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4332.