Development and validation of blood tumor mutational burden reference standards.

Tumor mutational burden (TMB), measured by exome or panel sequencing of tumor tissue or blood (bTMB), is a potential predictive biomarker for treatment benefit in patients with various cancer types receiving immunotherapy targeting checkpoint pathways. However, significant variability in TMB measurement has been observed. We developed contrived bTMB reference materials using DNA from tumor cell lines and donor-matched lymphoblastoid cell lines to support calibration and alignment across laboratories and platforms. Contrived bTMB reference materials were developed using genomic DNA from lung tumor cell lines blended into donor-matched lymphoblastoid cell lines at 0.5% and 2% tumor content, fragmented and size-selected to mirror the size profile of circulating cell-free tumor DNA with TMB scores of 7, 9, 20, and 26 mut/Mb. Variant allele frequency (VAF) and bTMB scores were assessed using PredicineATLAS and GuardantOMNI next-generation sequencing assays. DNA fragment sizes in the contrived reference samples were similar to those found within patient plasma-derived cell-free DNA, and mutational patterns aligned with those in the parental tumor lines. For the 7, 20, and 26 mut/Mb contrived reference samples with 2% tumor content, bTMB scores estimated using either assay aligned with expected scores from the parental tumor cell lines and showed good reproducibility. A bioinformatic filtration step was required to account for low-VAF artifact variants. We demonstrate the feasibility and challenges of producing and using bTMB reference standards across a range of bTMB levels, and how such standards could support the calibration and validation of bTMB platforms and help harmonization between panels and laboratories.

Contrived Materials and a Data Set for the Evaluation of Liquid Biopsy Tests: A Blood Profiling Atlas in Cancer (BLOODPAC) Community Study.

The Blood Profiling Atlas in Cancer (BLOODPAC) Consortium is a collaborative effort involving stakeholders from the public, industry, academia, and regulatory agencies focused on developing shared best practices on liquid biopsy. This report describes the results from the JFDI (Just Freaking Do It) study, a BLOODPAC initiative to develop standards on the use of contrived materials mimicking cell-free circulating tumor DNA, to comparatively evaluate clinical laboratory testing procedures. Nine independent laboratories tested the concordance, sensitivity, and specificity of commercially available contrived materials with known variant-allele frequencies (VAFs) ranging from 0.1% to 5.0%. Each participating laboratory utilized its own proprietary evaluation procedures. The results demonstrated high levels of concordance and sensitivity at VAFs of >0.1%, but reduced concordance and sensitivity at a VAF of 0.1%; these findings were similar to those from previous studies, suggesting that commercially available contrived materials can support the evaluation of testing procedures across multiple technologies. Such materials may enable more objective comparisons of results on materials formulated in-house at each center in multicenter trials. A unique goal of the collaborative effort was to develop a data resource, the BLOODPAC Data Commons, now available to the liquid-biopsy community for further study. This resource can be used to support independent evaluations of results, data extension through data integration and new studies, and retrospective evaluation of data collection.

High sensitivity to transmission of attenuation in the metal activating effect of platinum on sequential H-D exchange at the four C8 sites of inosines in tetrakis(inosine)platinum(II) chloride.

Hydrogen-deuterium exchange of the carbon-bound C(8)-H protons of the inosine residues in tetrakis(inosine)platinum(ii) chloride, S, with Pt binding at N(7), was studied in aqueous buffer solutions at 60 degrees C by (1)H NMR spectroscopy. The kinetics at all four C(8) sites as a function of pD of the D(2)O/OD(-) medium was measured through the disappearance of the C(8)-H signal, which yielded the pseudo first-order rate constant for exchange, k(obs). Plots of k(obs)versus [OD(-)] showed curvature reminiscent of saturation type kinetics and indicative of competitive deprotonation of N(1)-H sites. In contrast, the analogous N(1)-methylated cis-bis(1-methylinosine)diammineplatinum(ii) chloride leads to a linear k(obs)versus [OD(-)] plot. The potentiometrically determined macroscopic composite N(1)-H ionization constant was further dissected into the successive microscopic N(1)-H acidity constants of the four inosine residues of the complex S. The k(obs) values were also deconvoluted into individual rate constants k(ex) (i.e.k(0), k(1), k(2), k(3) for exchange of the successively deprotonated inosine moieties, S, S(1), S(2), S(3), it being assumed that S(4) where all four inosine ligands are deprotonated at N(1) is unreactive ("immunized") to exchange. The k(ex) values show a progressive attenuation in Pt activation of H-D exchange along the series, k(0), k(1), k(2), k(3). The k(ex) data thus generated, together with the deconvoluted individual pK(a) values allow the construction of the plot, log k(ex) [C(8)-H] vs. pK(a) [N(H)-1]. Remarkably, this plot exhibits good linearity (R(2) = 0.99), which accords this as a linear free energy relationship (LFER). The large negative slope value (-2.3) of this LFER reflects the high sensitivity of transmission of electron density from the ionized N(1) via Pt and/or through space to the remaining C(8)-H sites. This is to our knowledge the first instance in which a LFER is generated through modulation of a structure in a single molecule. One can anticipate that this approach may lead to: (1) predicting N-H acidity; (2) C-H H-D exchange susceptibility in a range of metal-biomolecule complexes; (3) their carbon acidity.

Beta-diketo acid pharmacophore hypothesis. 1. Discovery of a novel class of HIV-1 integrase inhibitors.

HIV-1 Integrase (IN) is an essential enzyme for viral replication. The discovery of beta-diketo acids was crucial in the validation of IN as a legitimate target in drug discovery against HIV infection. In this study, we discovered a novel class of IN inhibitors using a 3D pharmacophore guided database search. We used S-1360 (1), the first IN inhibitor to undergo clinical trials, and three other analogues to develop a common feature pharmacophore hypothesis. Testing this four-featured pharmacophore against a multiconformational database of 150,000 structurally diverse small molecules yielded 1,700 compounds that satisfied the 3D query. Subsequently, all 1,700 compounds were docked into the active site of IN. On the basis of docking scores, Lipinski's rule-of-five, and structural novelty, 110 compounds were selected for biological screening. We found that compounds that contain both salicylic acid and a 2-thioxo-4-thiazolidinone (rhodanine) group (e.g. 5-13) showed significant inhibitory potency against IN, while the presence of either salicylic acid or a rhodanine group alone did not. Although some of the compounds containing only a salicylic acid showed inhibitory potency against IN, none of the compounds containing only rhodanine exhibited considerable potency. Of the 52 compounds reported in this study, 11 compounds (5, 6, 8, 10-13, 32-33, 51, and 53) inhibited 3'-processing or strand transfer activities of IN with IC(50) < or = 25 microM. This is the first reported use of S-1360 and its analogues as leads in developing a pharmacophore hypothesis for IN inhibition and for identification of new compounds with potent inhibition of this enzyme.

Pharmacophore modeling and three dimensional database searching for drug design using catalyst: recent advances.

Perceiving a pharmacophore is the first essential step towards understanding the interaction between a receptor and a ligand. Once a pharmacophore is established, a beneficial use of it is 3D database searching to retrieve novel compounds that would match the pharmacophore. As the 3D searching technology has evolved over the years, it has been effectively used for lead optimization, combinatorial library focusing, as well as virtual high-throughput screening. This paper is an update to the original paper published in this journal earlier: Kurogi, Y, and Guner, O. F. "Pharmacophore Modeling and Three-Dimensional Database Searching for Drug Design Using Catalyst," in Current Medicinal Chemistry, 2001, 8(9), 1035-1055.

Three dimensional pharmacophore modeling of human CYP17 inhibitors. Potential agents for prostate cancer therapy.

We report here a molecular modeling investigation of steroidal and nonsteroidal inhibitors of human cytochrome P450 17alpha-hydroxylase-17,20-lyase (CYP17). Using the pharmacophore perception technique, we have generated common-feature pharmacophore model(s) to explain the putative binding requirements for two classes of human CYP17 inhibitors. Common chemical features in the steroid and nonsteroid human CYP17 enzyme inhibitors, as deduced by the Catalyst/HipHop program, are one to two hydrogen bond acceptors (HBAs) and three hydrophobic groups. For azole-steroidal ligands, the 3beta-OH group of ring A and the N-3 of the azole ring attached to ring D at C-17 act as hydrogen bond acceptors. A model that permits hydrogen bond interaction between the azole functionality on ring D and the enzyme is consistent with experimental deductions for type II CYP17 inhibitors where a sixth ligating atom interacts with Fe(II) of heme. In general, pharmacophore models derived for steroid and nonsteroidal compounds bear striking similarities to all azole sites mapping the HBA functionality and to three hydrophobic features describing the hydrophobic interactions between the ligands and the enzyme. Using the pharmacophore model derived for azole-steroidal inhibitors as a 3D search query against several 3D multiconformational Catalyst formatted databases, we identified several steroidal compounds with potential inhibition of this enzyme. Biological testing of some of these compounds show low to high inhibitory potency against the human CYP17 enzyme. This shows the potential of our pharmacophore model in identifying new and potent CYP17 inhibitors. Further refinement of the model is in progress with a view to identifying and optimizing new leads.

Novel retinoic acid metabolism blocking agents endowed with multiple biological activities are efficient growth inhibitors of human breast and prostate cancer cells in vitro and a human breast tumor xenograft in nude mice.

Novel retinoic acid metabolism blocking agents (RAMBAs) have been synthesized and characterized. The synthetic features include introduction of nucleophilic ligands at C-4 of all-trans-retinoic acid (ATRA) and 13-cis-retinoic acid, and modification of terminal carboxylic acid group. Most of our compounds are powerful inhibitors of hamster liver microsomal ATRA metabolism enzyme(s). The most potent compound is methyl (2E,4E,6E,8E)-9-(3-imidazolyl-2,6,6-trimethylcyclohex-1-enyl)-3,7-dimethylnona-2,4,6,8-tetraenoate (5) with an IC(50) value of 0.009 nM, which is 666,667 times more potent than the well-known RAMBA, liarozole (Liazal, IC(50) = 6000 nM). Quite unexpectedly, there was essentially no difference between the enzyme inhibitory activities of the two enantiomers of compound 5. In MCF-7 cell proliferation assays, the RAMBAs also enhance the ATRA-mediated antiproliferative activity in a concentration dependent manner. The novel atypical RAMBAs, in addition to being highly potent inhibitors of ATRA metabolism in microsomal preparations and in intact human cancer cells (MCF-7, T47D, and LNCaP), also exhibit multiple biological activities, including induction of apoptosis and differentiation, retinoic acid receptor binding, and potent antiproliferative activity on a number of human cancer cells. Following subcutaneous administration to mice bearing human breast MCF-7 tumor xenografts, 6 (VN/14-1, the free carboxylic acid of 5) was well-tolerated and caused significant tumor growth suppression ( approximately 85.2% vs control, p = 0.022). Our RAMBAs represent novel anticancer agents with unique multiple mechanisms of action. The most potent compounds are strong candidates for development as therapeutic agents for the treatment of a variety of cancers.

First chemical feature-based pharmacophore modeling of potent retinoidal retinoic acid metabolism blocking agents (RAMBAs): identification of novel RAMBA scaffolds.

The first three-dimensional (3D) pharmacophore model was developed for potent retinoidal retinoic acid metabolism blocking agents (RAMBAs) with IC(50) values ranging from 0.0009 to 5.84nM. The seven common chemical features in these RAMBAs as deduced by the Catalyst/HipHop program include five hydrophobic groups (hydrophobes), and two hydrogen bond acceptors. Using the pharmacophore model as a 3D search query against NCI and Maybridge conformational Catalyst formatted databases; we retrieved several compounds with different structures (scaffolds) as hits. Twenty-one retrieved hits were tested for RAMBA activity at 100nM concentration. The most potent of these compounds, NCI10308597 and HTS01914 showed inhibitory potencies less (54.7% and 53.2%, respectively, at 100nM) than those of our best previously reported RAMBAs VN/12-1 and VN/14-1 (90% and 86%, respectively, at 100nM). Docking studies using a CYP26A1 homology model revealed that our most potent RAMBAs showed similar binding to the one observed for a series of RAMBAs reported previously by others. Our data shows the potential of our pharmacophore model in identifying structurally diverse and potent RAMBAs. Further refinement of the model and searches of other robust databases is currently in progress with a view to identifying and optimizing new leads.

First pharmacophore-based identification of androgen receptor down-regulating agents: discovery of potent anti-prostate cancer agents.

A qualitative 3D pharmacophore model (a common feature based model or Catalyst HipHop algorithm) was developed for well-known natural product androgen receptor down-regulating agents (ARDAs). The four common chemical features identified included: one hydrophobic group, one ring aromatic group, and two hydrogen bond acceptors. This model served as a template in virtual screening of the Maybridge and NCI databases that resulted in identification of six new ARDAs (EC(50) values 17.5-212 microM). Five of these molecules strongly inhibited the growth of human prostate LNCaP cells. These novel compounds may be used as leads to develop other novel anti-prostate cancer agents.

Possibilities for transfer of relevant data without revealing structural information.

In this paper, we discuss how we safely exchanged proprietary data between third parties in the early years of predictive ADME/Tox model development. At that time, industry scientists wanted to evaluate predictive models, but were not willing to share their structures with software vendors. At the same time, model developers were willing to run the scientists' structures through their models, but they were not willing to reveal which descriptors were important for a particular predictive model. We developed a process where scientists could perform calculations on a broad number of commercially available public descriptors and forward results as a property file, instead of their structures. Meanwhile, the model developer could extract descriptors used in the predictive model, run the model, and pass results back to the scientist. On the following pages, we discuss the pros and cons of this approach, and we address questions such as: Can structural information that is proprietary be compromised from descriptors in ADME/Tox models? Can ADME/Tox predictions be made purely from descriptors, without the explicit knowledge of chemical structures, proprietary or otherwise?