Physicochemical property profiles of marketed drugs, clinical candidates and bioactive compounds

Developability profile framework for lead candidate selection in topical dermatology

The development of molecules for topical dermatology has primarily relied on drug repurposing or on combination therapies, leading to an average of only one New Chemical Entity (NCE) approved per year by the FDA. Topical products offer benefits to patients by enabling localized treatment, while minimizing systemic exposure and the likelihood of adverse events. New therapies are further justified by the burden skin diseases cause on patients' quality of life. Notwithstanding the opportunities, the selection of a topical NCE presents challenges, primarily derived from a target product profile uncommon to oral drugs. Beyond a more stringent range of physicochemical properties, the molecule must display adequate solubility and chemical stability in topical-relevant excipients; must effectively cross the stratum corneum, considerably less permeable than the intestinal epithelium, and elicit a local therapeutic response; and must enable a formulation with robust physical stability. A novel framework intended to de-risk NCE selection is presented and based on four calculated physicochemical properties: molecular weight, clogP, topological polar surface area, and aromatic ring count. The use of topical-relevant solvents to assess the molecule's solubility profile, and a 2-day accelerated chemical stability methodology, are also described as critical steps in early dermal development.

Physicochemical Profiling and Comparison of Research Antiplasmodials and Advanced Stage Antimalarials with Oral Drugs

To understand the property space of antimalarials, we collated a large dataset of research antiplasmodial (RAP) molecules with known in vitro potencies and advanced stage antimalarials (ASAMs) with established oral bioavailability. While RAP molecules are "non-druglike", ASAM molecules display properties closer to Lipinski's and Veber's thresholds. Comparison within the different potency groups of RAP molecules indicates that the in vitro potency is positively correlated to the molecular weight, the calculated octanol-water partition coefficient (clog P), aromatic ring counts (#Ar), and hydrogen bond acceptors. Despite both categories being bioavailable, the ASAM molecules are relatively larger and more lipophilic, have a lower polar surface area, and possess a higher count of heteroaromatic rings than oral drugs. Also, antimalarials are found to have a higher proportion of aromatic (#ArN) and basic nitrogen (#BaN) counts, features implicitly used in the design of antimalarial molecules but not well studied hitherto. We also propose using descriptors scaled by the sum of #ArN and #BaN (SBAN) to define an antimalarial property space. Together, these results may have important applications in the identification and optimization of future antimalarials.

Thermodynamic profiling for fragment-based lead discovery and optimization

Introduction: The enthalpic and entropic components of the ligand-protein binding free energy reflect the type and quality of the interactions and relate to the physicochemical properties of the ligands. These findings have significance in medicinal chemistry optimizations since they suggest that the thermodynamic profiling of the binding may help monitor and control the unfavorable size and hydrophobicity increase typically accompanying affinity improvements and leading to suboptimal pharmacokinetic properties.Areas covered: This review describes the ligand-protein binding event in terms of elementary steps, their associated interactions, and their enthalpic and entropic consequences. The relationships among the breaking and forming interactions, the binding thermodynamic profile, and the physicochemical properties of the ligands are also discussed.Expert opinion: Analysis of the size dependence of available affinity and favorable enthalpy highlights the limitation of the simultaneous optimization of these quantities. Indeed, moderate, rather than very high affinities can be conciliated with favorable physicochemical and pharmacokinetic profiles as it is supported by the affinity range of historical oral drugs. Although thermodynamic quantities are not suitable endpoints for medicinal chemistry optimizations owing to the complexity of the binding thermodynamics, thermodynamic profiling together with structural studies can be advantageously used to understand the details of the binding process and to optimize it.

Development of Cassette PAMPA for Permeability Screening

The parallel artificial membrane permeability assay (PAMPA) is widely used in early-stage drug discovery to discriminate compounds by intestinal permeability. The purpose of the current study was to establish a cassette (n-in-1) PAMPA to enable permeability screening of lipophilic compounds. A double-sink PAMPA consisting of a pH gradient (i.e., pH 6.5 and 7.4 for the donor and receiver compartments, respectively) and a lipophilic sink (i.e., a surfactant in the receiver solution) was utilized with cassette incubation of 10 reference compounds. Sample analysis was conducted using selected reaction monitoring (SRM) with a triple quadrupole LC-MS/MS system. Correlation between PAMPA permeability and human intestinal absorption (HIA) of the reference compounds yielded two false negatives, namely propranolol (PPN) and verapamil (VER); these two compounds showed a substantially lower recovery (ca. 10%) than other reference compounds (>69%). This cassette PAMPA was repeated subsequently with polysorbate 80 added to the donor compartments, which resulted in a significant increase in both the recovery and the permeability of the false negatives. Accordingly, the permeability class of all reference compounds could be unambiguously differentiated using this cassette PAMPA. Also, a strong linear correlation (r=0.9845) was observed between the cassette and discrete permeability of all reference compounds.

Compound high-quality criteria: a new vision to guide the development of drugs, current situation

For several decades, the pharmaceutical industry has suffered due to major issues such as reductions of the number of FDA approved drugs and biologics. Several analyses have been highlighted that the 'druglikeness' is one of the strategies to improve succeed rates of screening such as, for instance, high-throughput screening (HTS), and then hits (as starting point), leads and clinical candidates. It is clear that the improvement of compound quality accelerates the drug discovery projects. The monitoring of several indices to avoid 'molecular obesity' (ADMET problems) of final drugs from good-quality 'low-fat' starting points represents today a powerful strategy of optimization process. The development of the new guides to find drugs highlighting attempts at improving the attrition rate from hits to final medicines by focusing on how to improve the druggability of hits, leads and drugs during the drug discovery process represents a key approach to design next better generation of medicines.

Increasing the delivery of next generation therapeutics from high throughput screening libraries

The pharmaceutical industry has historically relied on high throughput screening as a cornerstone to identify chemical equity for drug discovery projects. However, with pharmaceutical companies moving through a phase of diminished returns and alternative hit identification strategies proving successful, it is more important than ever to understand how this approach can be used more effectively to increase the delivery of next generation therapeutics from high throughput screening libraries. There is a wide literature that describes HTS and fragment based screening approaches which offer clear direction on the process for these two distinct activities. However, few people have considered how best to identify medium to low molecular weight compounds from large diversity screening sets and increase downstream success.

Comparing the Chemical Structure and Protein Content of ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database

ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database are resources of curated chemistry-to-protein relationships widely used in the chemogenomic arena. In this work we have extended an earlier analysis (PMID 22821596) by comparing chemistry and protein target content between 2010 and 2013. For the former, details are presented for overlaps and differences, statistics of stereochemistry as well as stereo representation and MW profiles between the four databases. For 2013 our results indicate quality improvements, major expansion, increased achiral structures and changes in MW distributions. An orthogonal comparison of chemical content with different sources inside PubChem highlights further interpretable differences. Expansion of protein content by UniProt IDs is also recorded for 2013 and Gene Ontology comparisons for human-only sets indicate differences. These emphasise the expanding complementarity of chemistry-to-protein relationships between sources, although different criteria are used for their capture.

Big pharma screening collections: more of the same or unique libraries? The AstraZeneca-Bayer Pharma AG case

In this study, the screening collections of two major pharmaceutical companies (AstraZeneca and Bayer Pharma AG) have been compared using a 2D molecular fingerprint by a nearest neighborhood approach. Results revealed a low overlap between both collections in terms of compound identity and similarity. This emphasizes the value of screening multiple compound collections to expand the chemical space that can be accessed by high-throughput screening (HTS).

An Investigation of the Relationship Between Molecular Topology and CYP3A4 Inhibition for Drug-like Compounds

Earlier studies have shown that CYP3A4 inhibition is influenced by lipophilicity, size and fMF , where fMF is the size of the molecular framework divided by the total size of the molecule. Other previous studies have shown that there is an enrichment of compounds with only one ring system among drugs in comparison to clinical candidates and general bioactive compounds as well as enrichment among natural products and human metabolites compared to general bioactive compounds. Thus a logical extension of the earlier investigations would be to study how ring systems influence CYP3A4 inhibition. It is shown here that compounds with only one ring system have lower CYP3A4 inhibition compared to compounds with several ring systems. The reason therefore is that they are both smaller and have smaller fMF values compared to compounds with several ring systems. The investigation provides additional insights into how a molecule should be constituted to have low CYP3A4 inhibition and may influence library design and compound acquisitions.

DrugLogit: logistic discrimination between drugs and nondrugs including disease-specificity by assigning probabilities based on molecular properties

The increasing knowledge of both structure and activity of compounds provides a good basis for enhancing the pharmacological characterization of chemical libraries. In addition, pharmacology can be seen as incorporating both advances from molecular biology as well as chemical sciences, with innovative insight provided from studying target-ligand data from a ligand molecular point of view. Predictions and profiling of libraries of drug candidates have previously focused mainly on certain cases of oral bioavailability. Inclusion of other administration routes and disease-specificity would improve the precision of drug profiling. In this work, recent data are extended, and a probability-based approach is introduced for quantitative and gradual classification of compounds into categories of drugs/nondrugs, as well as for disease- or organ-specificity. Using experimental data of over 1067 compounds and multivariate logistic regressions, the classification shows good performance in training and independent test cases. The regressions have high statistical significance in terms of the robustness of coefficients and 95% confidence intervals provided by a 1000-fold bootstrapping resampling. Besides their good predictive power, the classification functions remain chemically interpretable, containing only one to five variables in total, and the physicochemical terms involved can be easily calculated. The present approach is useful for an improved description and filtering of compound libraries. It can also be applied sequentially or in combinations of filters, as well as adapted to particular use cases. The scores and equations may be able to suggest possible routes for compound or library modification. The data is made available for reuse by others, and the equations are freely accessible at http://hermes.chem.ut.ee/~alfx/druglogit.html.

SARConnect: A Tool to Interrogate the Connectivity Between Proteins, Chemical Structures and Activity Data

The access and use of large-scale structure-activity relationships (SAR) is increasing as the range of targets and availability of bioactive compound-to-protein mappings expands. However, effective exploitation requires merging and normalisation of activity data, mappings to target classifications as well as visual display of chemical structure relationships. This work describes the development of the application "SARConnect" to address these issues. We discuss options for delivery and analysis of large-scale SAR data together with a set of use-cases to illustrate the design choices and utility. The main activity sources of ChEMBL,1 GOSTAR2 and AstraZeneca's internal system IBIS, had already been integrated in Chemistry Connect.3 For target relationships we selected human UniProtKB/Swiss-Prot4 as our primary source of a heuristic target classification. Similarly, to explore chemical relationships we combined several methods for framework and scaffold analysis into a unified, hierarchical classification where ease of navigation was the primary goal. An application was built on TIBCO Spotfire to retrieve data for visual display. Consequently, users can explore relationships between target, activity and structure across internal, external and commercial sources that encompass approximately 3 million compounds, 2000 human proteins and 10 million activity values. Examples showing the utility of the application are given.

Finding the sweet spot: the role of nature and nurture in medicinal chemistry

Given its position at the heart of small-molecule drug discovery, medicinal chemistry has an important role in tackling the well-known productivity challenges in pharmaceutical research and development. In recent years, extensive analyses of successful and failed discovery compounds and drug candidates have improved our understanding of the role of physicochemical properties in drug attrition. Based on the clarified challenges in finding the 'sweet spot' in medicinal chemistry programmes, we suggest that this goal can be achieved through a combination of first identifying chemical starting points with appropriate 'nature' and then rigorously 'nurturing' them during lead optimization. Here, we discuss scientific, strategic, organizational and cultural considerations for medicinal chemistry practices, with the aim of promoting more effective use of what is already known, as well as a wider appreciation of the risks of pursuing suboptimal compounds.

Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: Ranking 1654 human protein targets by assayed compounds and molecular scaffolds

Background

Since the classic Hopkins and Groom druggable genome review in 2002, there have been a number of publications updating both the hypothetical and successful human drug target statistics. However, listings of research targets that define the area between these two extremes are sparse because of the challenges of collating published information at the necessary scale. We have addressed this by interrogating databases, populated by expert curation, of bioactivity data extracted from patents and journal papers over the last 30 years.

Results

From a subset of just over 27,000 documents we have extracted a set of compound-to-target relationships for biochemical in vitro binding-type assay data for 1,736 human proteins and 1,654 gene identifiers. These are linked to 1,671,951 compound records derived from 823,179 unique chemical structures. The distribution showed a compounds-per-target average of 964 with a maximum of 42,869 (Factor Xa). The list includes non-targets, failed targets and cross-screening targets. The top-278 most actively pursued targets cover 90% of the compounds. We further investigated target ranking by determining the number of molecular frameworks and scaffolds. These were compared to the compound counts as alternative measures of chemical diversity on a per-target basis.

Conclusions

The compounds-per-protein listing generated in this work (provided as a supplementary file) represents the major proportion of the human drug target landscape defined by published data. We supplemented the simple ranking by the number of compounds assayed with additional rankings by molecular topology. These showed significant differences and provide complementary assessments of chemical tractability.

Probing the links between in vitro potency, ADMET and physicochemical parameters

A common underlying assumption in current drug discovery strategies is that compounds with higher in vitro potency at their target(s) have greater potential to translate into successful, low-dose therapeutics. This has led to the development of screening cascades with in vitro potency embedded as an early filter. However, this approach is beginning to be questioned, given the bias in physicochemical properties that it can introduce early in lead generation and optimization, which is due to the often diametrically opposed relationship between physicochemical parameters associated with high in vitro potency and those associated with desirable absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Here, we describe analyses that probe these issues further using the ChEMBL database, which includes more than 500,000 drug discovery and marketed oral drug compounds. Key findings include: first, that oral drugs seldom possess nanomolar potency (50 nM on average); second, that many oral drugs have considerable off-target activity; and third, that in vitro potency does not correlate strongly with the therapeutic dose. These findings suggest that the perceived benefit of high in vitro potency may be negated by poorer ADMET properties.

Development of small-molecule therapies for autoimmune diseases

Until the recent advent of genetically engineered drugs, small molecules constituted the predominant method of treatment for autoimmune diseases. Both modalities have advantages and disadvantages; while protein-based therapeutics interfere very selectively with the function of their biological targets, they have to be administered subcutaneously or intravenously. Small molecules have the potential for oral administration. Due to their cell permeability, they can interact with extra- and intracellular targets, thus opening opportunities for interfering with novel biochemical pathways. We herein describe the preclinical stages of typical small-molecule research programmes and outline hurdles that may have to be overcome. A few examples of small molecules that are currently under clinical evaluation and arose from diverse discovery pathways will be discussed.

Unintended potential impact of perfect sink conditions on PLGA degradation in microparticles

Yet, no standardized test method for drug release measurements from PLGA-based microparticles has been generally agreed on, or described by the regulatory authorities. Often, perfect sink conditions are provided in vitro to avoid artificial drug saturation effects. However, the maintenance of such conditions might strongly affect PLGA degradation. The involved physicochemical processes are complex and the potential impact of perfect sink conditions is not yet well understood. Differently sized, highly porous, carbamazepine- and ibuprofen-loaded PLGA microparticles were prepared by a W/O/W emulsion solvent extraction/evaporation technique. The initial drug loading was intentionally low (3-4%) so that the two drugs were molecularly dispersed within the polymeric matrices (monolithic solutions). This was important to be able to exclude potential limited drug solubility effects on the resulting release kinetics. Drug release into phosphate buffer pH 7.4 was measured under perfect sink conditions. SEC, DSC and SEM were used to characterize polymer degradation. The decrease in the average polymer molecular weight, glass transition temperature as well as changes in the inner and outer morphology of the PLGA microparticles were strongly affected by the bulk fluid's volume. In the case of the poorly water-soluble drug carbamazepine, much lower "microparticle mass:phosphate buffer volume" ratios were required to maintain perfect sink conditions, resulting in stable pH values within the bulk fluid, slower PLGA degradation and, thus, lower drug release rates. Thus, great care has to be taken when defining the conditions for in vitro drug release measurements from PLGA-based microparticles, avoiding potentially artificial conditions for polymer degradation.

Affinity-based, biophysical methods to detect and analyze ligand binding to recombinant proteins: matching high information content with high throughput

Affinity-based technologies have become impactful tools to detect, monitor and characterize molecular interactions using recombinant target proteins. This can aid the understanding of biological function by revealing mechanistic details, and even more importantly, enables the identification of new improved ligands that can modulate the biological activity of those targets in a desired fashion. The selection of the appropriate technology is a key step in that process, as each one of the currently available technologies offers a characteristic type of biophysical information about the ligand-binding event. Alongside the indisputable advantages of each of those technologies they naturally display diverse restrictions that are quite frequently related to the target system to be studied but also to the affinity, solubility and molecular size of the ligands. This paper discusses some of the theoretical and experimental aspects of the most common affinity-based methods, what type of information can be gained from each one of those approaches, and what requirements as well as limitations are expected from working with recombinant proteins on those platforms and how those can be optimally addressed.