6 research outputs found
Rules for Identifying Potentially Reactive or Promiscuous Compounds
This article describes a set of 275 rules, developed
over an 18-year
period, used to identify compounds that may interfere with biological
assays, allowing their removal from screening sets. Reasons for rejection
include reactivity (e.g., acyl halides), interference with assay measurements
(fluorescence, absorbance, quenching), activities that damage proteins
(oxidizers, detergents), instability (e.g., latent aldehydes), and
lack of druggability (e.g., compounds lacking both oxygen and nitrogen).
The structural queries were profiled for frequency of occurrence in
druglike and nondruglike compound sets and were extensively reviewed
by a panel of experienced medicinal chemists. As a means of profiling
the rules and as a filter in its own right, an index of biological
promiscuity was developed. The 584 gene targets with screening data
at Lilly were assigned to 17 subfamilies, and the number of subfamilies
at which a compound was active was used as a promiscuity index. For
certain compounds, promiscuous activity disappeared after sample repurification,
indicating interference from occult contaminants. Because this type
of interference is not amenable to substructure search, a “nuisance
list” was developed to flag interfering compounds that passed
the substructure rules
Rules for Identifying Potentially Reactive or Promiscuous Compounds
This article describes a set of 275 rules, developed
over an 18-year
period, used to identify compounds that may interfere with biological
assays, allowing their removal from screening sets. Reasons for rejection
include reactivity (e.g., acyl halides), interference with assay measurements
(fluorescence, absorbance, quenching), activities that damage proteins
(oxidizers, detergents), instability (e.g., latent aldehydes), and
lack of druggability (e.g., compounds lacking both oxygen and nitrogen).
The structural queries were profiled for frequency of occurrence in
druglike and nondruglike compound sets and were extensively reviewed
by a panel of experienced medicinal chemists. As a means of profiling
the rules and as a filter in its own right, an index of biological
promiscuity was developed. The 584 gene targets with screening data
at Lilly were assigned to 17 subfamilies, and the number of subfamilies
at which a compound was active was used as a promiscuity index. For
certain compounds, promiscuous activity disappeared after sample repurification,
indicating interference from occult contaminants. Because this type
of interference is not amenable to substructure search, a “nuisance
list” was developed to flag interfering compounds that passed
the substructure rules
Integration of in Silico and in Vitro Tools for Scaffold Optimization during Drug Discovery: Predicting P‑Glycoprotein Efflux
In silico tools are regularly utilized
for designing and prioritizing
compounds to address challenges related to drug metabolism and pharmacokinetics
(DMPK) during the process of drug discovery. P-Glycoprotein (P-gp)
is a member of the ATP-binding cassette (ABC) transporters with broad
substrate specificity that plays a significant role in absorption
and distribution of drugs that are P-gp substrates. As a result, screening
for P-gp transport has now become routine in the drug discovery process.
Typically, bidirectional permeability assays are employed to assess
in vitro P-gp efflux. In this article, we use P-gp as an example to
illustrate a well-validated methodology to effectively integrate in
silico and in vitro tools to identify and resolve key barriers during
the early stages of drug discovery. A detailed account of development
and application of in silico tools such as simple guidelines based
on physicochemical properties and more complex quantitative structure–activity
relationship (QSAR) models is provided. The tools were developed based
on structurally diverse data for more than 2000 compounds generated
using a robust P-gp substrate assay over the past several years. Analysis
of physicochemical properties revealed a significantly lower proportion
(<10%) of P-gp substrates among the compounds with topological
polar surface area (TPSA) <60 Ă…<sup>2</sup> and the most basic
cpKa <8. In contrast, this proportion of substrates was greater
than 75% for compounds with TPSA >60 Ă…<sup>2</sup> and the
most
basic cpKa >8. Among the various QSAR models evaluated to predict
P-gp efflux, the Bagging model provided optimum prediction performance
for prospective validation based on chronological test sets. Four
sequential versions of the model were built with increasing numbers
of compounds to train the models as new data became available. Except
for the first version with the smallest training set, the QSAR models
exhibited robust prediction profiles with positive prediction values
(PPV) and negative prediction values (NPV) exceeding 80%. The QSAR
model demonstrated better concordance with the manual P-gp substrate
assay than an automated P-gp substrate screen. The in silico and the
in vitro tools have been effectively integrated during early stages
of drug discovery to resolve P-gp-related challenges exemplified by
several case studies. Key learning based on our experience with P-gp
can be widely applicable across other DMPK-related challenges
Complexity-Based Metric for Process Mass Intensity in the Pharmaceutical Industry
Process mass intensity (PMI) is a key metric for evaluating
the
sustainability of a manufacturing process. Within Eli Lilly and Co.
(Lilly), a process based on the molecular complexity and the projected
market demand has been adopted to set PMI targets for prospective
drugs. This strategy is described. PMIs for relevant molecules from
publications in this journal were also calculated and compared to
the model. These data illustrate the strengths and weaknesses of the
model
Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set
Biochemical
assay interference is becoming increasingly recognized
as a significant waste of resource in drug discovery, both in industry
and academia. A seminal publication from Baell and Holloway raised
the awareness of this issue, and they published a set of alerts to
identify what they described as PAINS (pan-assay interference compounds).
These alerts have been taken up by drug discovery groups, even though
the original paper had a somewhat limited data set. Here, we have
taken Lilly’s far larger internal data set to assess the PAINS
alerts on four criteria: promiscuity (over six assay formats including
AlphaScreen), compound stability, cytotoxicity, and presence of a
high Hill slope as a surrogate for non-1:1 protein–ligand binding.
It was found that only three of the alerts show pan-assay promiscuity,
and the alerts appear to encode primarily AlphaScreen promiscuous
molecules. Although not enriching for pan-assay promiscuity, many
of the alerts do encode molecules that are unstable, show cytotoxicity,
and increase the prevalence of high Hill slopes
Selectivity Data: Assessment, Predictions, Concordance, and Implications
Could
high-quality in silico predictions in drug discovery eventually
replace part or most of experimental testing? To evaluate the agreement
of selectivity data from different experimental or predictive sources,
we introduce the new metric concordance minimum significant ratio
(cMSR). Empowered by cMSR, we find the overall level of agreement
between predicted and experimental data to be comparable to that found
between experimental results from different sources. However, for
molecules that are either highly selective or potent, the concordance
between different experimental sources is significantly higher than
the concordance between experimental and predicted values. We also
show that computational models built from one data set are less predictive
for other data sources and highlight the importance of bias correction
for assessing selectivity data. Finally, we show that small-molecule
target space relationships derived from different data sources and
predictive models share overall similarity but can significantly differ
in details