QSAR without borders Muratov, Eugene N; Bajorath, Jürgen; Sheridan, Robert P ...
Chemical Society reviews,
06/2020, Volume:
49, Issue:
11
Journal Article
Peer reviewed
Open access
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in ...chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
Word cloud summary of diverse topics associated with QSAR modeling that are discussed in this review.
While drug discovery scientists take heed of various guidelines concerning drug-like character, the influence of acid/base properties often remains under-scrutinised. Ionisation constants (p
K
a
...values) are fundamental to the variability of the biopharmaceutical characteristics of drugs and to underlying parameters such as logD and solubility. p
K
a
values affect physicochemical properties such as aqueous solubility, which in turn influences drug formulation approaches. More importantly, absorption, distribution, metabolism, excretion and toxicity (ADMET) are profoundly affected by the charge state of compounds under varying pH conditions. Consideration of p
K
a
values in conjunction with other molecular properties is of great significance and has the potential to be used to further improve the efficiency of drug discovery. Given the recent low annual output of new drugs from pharmaceutical companies, this review will provide a timely reminder of an important molecular property that influences clinical success.
This tutorial review discusses the acid/base character of drugs and its influence on drug toxicity, physicochemical properties and biopharmaceutical behaviour.
Most rare diseases still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic ...incentives to catalyse the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. With this in mind, we discuss here the technological basis and rare disease applicability of the main therapeutic modalities, including small molecules, monoclonal antibodies, protein replacement therapies, oligonucleotides and gene and cell therapies, as well as drug repurposing. For each modality, we consider its strengths and limitations as a platform for rare disease therapy development and describe clinical progress so far in developing drugs based on it. We also discuss selected overarching topics in the development of therapies for rare diseases, such as approval statistics, engagement of patients in the process, regulatory pathways and digital tools.
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FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Abstract
In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that ...can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein–protein interactions, protein–disease and protein–phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.
Surely you are joking, Mr Docking Gentile, F; Oprea, T. I; Tropsha, A ...
Chemical Society reviews,
02/2023, Volume:
52, Issue:
3
Journal Article
Peer reviewed
In the wake of recent COVID-19 pandemics scientists around the world rushed to deliver numerous CADD (Computer-Aided Drug Discovery) methods and tools that could be reliably used to discover novel ...drug candidates against the SARS-CoV-2 virus. With that, there emerged a trend of a significant democratization of CADD that contributed to the rapid development of various COVID-19 drug candidates currently undergoing different stages of validation. On the other hand, this democratization also inadvertently led to the surge rapidly performed molecular docking studies to nominate multiple scores of novel drug candidates supported by computational arguments only. Albeit driven by best intentions, most of such studies also did not follow best practices in the field that require experience and expertise learned through multiple rigorously designed benchmarking studies and rigorous experimental validation. In this Viewpoint we reflect on recent disbalance between small number of rigorous and comprehensive studies and the proliferation of purely computational studies enabled by the ease of docking software availability. We further elaborate on the hyped oversale of CADD methods' ability to rapidly yield viable drug candidates and reiterate the critical importance of rigor and adherence to the best practices of CADD in view of recent emergence of AI and Big Data in the field.
In the wake of recent COVID-19 pandemics scientists around the world rushed to deliver numerous CADD (Computer-Aided Drug Discovery) methods and tools that could be reliably used to discover novel drug candidates against the SARS-CoV-2 virus.
The Biopharmaceutics Drug Disposition Classification system (BDDCS) is a four-class approach based on water solubility and extent of metabolism/permeability rate. Based on the BDDCS class to which a ...drug is assigned, it is possible to predict the role of metabolic enzymes and transporters on the drug disposition of a new molecular entity (NME) prior to its administration to animals or humans. Here, we report a total of 1475 drugs and active metabolites to which the BDDCS is applied. Of these, 379 are new entries, and 1096 are revisions of former classification studies with the addition of references for the approved maximum dose strength, extent of the systemically available drug excreted unchanged in the urine, and lowest solubility over the pH range 1.0–6.8 when such information is available in the literature. We detail revised class assignments of previously misclassified drugs and the literature analyses to classify new drugs. We review the process of solubility assessment for NMEs prior to drug dosing in humans and approved dose classification, as well as the comparison of Biopharmaceutics Classification System (BCS)
versus
BDDCS assignment. We detail the uses of BDDCS in predicting, prior to dosing animals or humans, disposition characteristics, potential brain penetration, food effect, and drug-induced liver injury (DILI) potential. This work provides an update on the current status of the BDDCS and its uses in the drug development process.
Graphical abstract
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
DrugCentral monitors new drug approvals and standardizes drug information. The current update contains 285 drugs (131 for human use). New additions include: (i) the integration of veterinary drugs ...(154 for animal use only), (ii) the addition of 66 documented off-label uses and iii) the identification of adverse drug events from pharmacovigilance data for pediatric and geriatric patients. Additional enhancements include chemical substructure searching using SMILES and 'Target Cards' based on UniProt accession codes. Statistics of interests include the following: (i) 60% of the covered drugs are on-market drugs with expired patent and exclusivity coverage, 17% are off-market, and 23% are on-market drugs with active patents and exclusivity coverage; (ii) 59% of the drugs are oral, 33% are parenteral and 18% topical, at the level of the active ingredients; (iii) only 3% of all drugs are for animal use only; however, 61% of the veterinary drugs are also approved for human use; (iv) dogs, cats and horses are by far the most represented target species for veterinary drugs; (v) the physicochemical property profile of animal drugs is very similar to that of human drugs. Use cases include azaperone, the only sedative approved for swine, and ruxolitinib, a Janus kinase inhibitor.
BDDCS Applied to Over 900 Drugs Benet, Leslie Z.; Broccatelli, Fabio; Oprea, Tudor I.
The AAPS journal,
12/2011, Volume:
13, Issue:
4
Journal Article
Peer reviewed
Open access
Here, we compile the Biopharmaceutics Drug Disposition Classification System (BDDCS) classification for 927 drugs, which include 30 active metabolites. Of the 897 parent drugs, 78.8% (707) are ...administered orally. Where the lowest measured solubility is found, this value is reported for 72.7% (513) of these orally administered drugs and a dose number is recorded. The measured values are reported for percent excreted unchanged in urine, Log
P
, and Log
D
7.4
when available. For all 927 compounds, the
in silico
parameters for predicted Log solubility in water, calculated Log
P
, polar surface area, and the number of hydrogen bond acceptors and hydrogen bond donors for the active moiety are also provided, thereby allowing comparison analyses for both
in silico
and experimentally measured values. We discuss the potential use of BDDCS to estimate the disposition characteristics of novel chemicals (new molecular entities) in the early stages of drug discovery and development. Transporter effects in the intestine and the liver are not clinically relevant for BDDCS class 1 drugs, but potentially can have a high impact for class 2 (efflux in the gut, and efflux and uptake in the liver) and class 3 (uptake and efflux in both gut and liver) drugs. A combination of high dose and low solubility is likely to cause BDDCS class 4 to be underpopulated in terms of approved drugs (
N
= 53 compared with over 200 each in classes 1–3). The influence of several measured and
in silico
parameters in the process of BDDCS category assignment is discussed in detail.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly ...available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD.
ChemProt is a publicly available compilation of chemical-protein-disease annotation resources that enables the study of systems pharmacology for a small molecule across multiple layers of complexity ...from molecular to clinical levels. In this third version, ChemProt has been updated to more than 1.7 million compounds with 7.8 million bioactivity measurements for 19,504 proteins. Here, we report the implementation of global pharmacological heatmap, supporting a user-friendly navigation of chemogenomics space. This facilitates the visualization and selection of chemicals that share similar structural properties. In addition, the user has the possibility to search by compound, target, pathway, disease and clinical effect. Genetic variations associated to target proteins were integrated, making it possible to plan pharmacogenetic studies and to suggest human response variability to drug. Finally, Quantitative Structure-Activity Relationship models for 850 proteins having sufficient data were implemented, enabling secondary pharmacological profiling predictions from molecular structure. Database URL: http://potentia.cbs.dtu.dk/ChemProt/.
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