An automated immunoassay for early specificity profiling of antibodies

Antibody-based therapeutics are of great value for the treatment of human diseases. In addition to functional activity, affinity or physico-chemical properties, antibody specificity is considered to be one of the most crucial attributes for safety and efficacy. Consequently, appropriate studies are required before entering clinical trials.

High content protein arrays are widely applied to assess antibody specificity, but this commercial solution can only be applied to final therapeutic antibody candidates because such arrays are expensive and their throughput is limited. A flexible, high-throughput and economical assay that allows specificity testing of IgG or Fab molecules during early discovery is described here. The 384-well microtiter plate assay contains a comprehensive panel of 32 test proteins and uses electrochemiluminescence as readout.

The Protein Panel Profiling (3P) was used to analyze marketed therapeutic antibodies that all showed highly specific binding profiles. Subsequently, 3P was applied to antibody candidates from early discovery and the results compared well with those obtained with a commercially available high content protein chip. Our results suggest that 3P can be applied as an additional filter for lead selection, allowing the identification of favorable antibody candidates in early discovery and thereby increasing the speed and possibility of success in drug development.     

The integration of investigative toxicology in the drug discovery process

Summary— Integrating toxicology early in the drug discovery process adds value by providing the earliest possible identification of a compound's potential for toxicological and pathological effects relevant to intended clinical use. With this approach true ‘lead’ candidates, with a high probability of clinical success, are identified and advanced while reducing effort and resources expended on compounds without the requisite therapeutic index. Resources are focussed on the speed of getting a discovery ‘lead’ into early clinical development, defining the mechanisms of observed preclinical toxicity and their relevance to human use, and developing early safety data with in vitro test systems ahead of in vivo systems where possible, thus reducing animal use.     

Applications of high-throughput ADME in drug discovery

Assessment of physicochemical and pharmacological properties is now conducted at very early stages of drug discovery for the purpose of accelerating the conversion of hits and leads into qualified development candidates. In particular, in vitro absorption, distribution, metabolism and elimination (ADME) assays and in vivo drug metabolism pharmacokinetic (DMPK) studies are being conducted throughout the discovery process, from hit generation through to lead optimization, with the goal of reducing the attrition rate of these potential drug candidates as they progress through development. Because the continuing trend in drug discovery has been to access ADME information earlier and earlier in the discovery process, the need has arisen within the analytical community to introduce faster and better analytical methods to enhance the 'developability' of drug leads. Strategies for streamlined ADME assessment of drug candidates in discovery and pre-clinical development are presented within.     

Recent Advances in Physicochemical and ADMET Profiling in Drug Discovery

The drastic increase in the cost for discovering and developing a new drug along with the high attrition rate of development candidates led to shifting drug‐discovery strategy to parallel assessment of comprehensive drug physicochemical, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties alongside efficacy. With the proposal of a profiling paradigm and utilization of integrated risk assessment, one can exponentially enhance the predictive power of in vitro tools by taking into consideration the interplay among profiling parameters. In particular, this article will review recent advances in accurate assessment of solubility and other physicochemical parameters. The proper interpretation of these experimental data is crucial for rapid and meaningful risk assessment and rational optimization of drug candidates in drug discovery. The impact of these tools on assisting drug‐discovery teams in establishing in vitro–in vivo correlation (IVIVC) as well as structure–property relationship (SPR) will be presented.     

In silico optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a ‘parallel progression approach to drug design’ applied to β-blockers

The present work exploits the potential of in silico approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein ‘parallel’ information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the ‘parallel progression approach’. This approach utilizes molecular modeling tools viz. hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log V_d) and clearance (log Cl), which together influence the half-life (t_1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant pK_i have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and pK_i values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through in silico methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the ‘parallel progression approach’ helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.     

Utility of a human FcRn transgenic mouse model in drug discovery for early assessment and prediction of human pharmacokinetics of monoclonal antibodies

Therapeutic antibodies continue to develop as an emerging drug class, with a need for preclinical tools to better predict in vivo characteristics. Transgenic mice expressing human neonatal Fc receptor (hFcRn) have potential as a preclinical pharmacokinetic (PK) model to project human PK of monoclonal antibodies (mAbs). Using a panel of 27 mAbs with a broad PK range, we sought to characterize and establish utility of this preclinical animal model and provide guidance for its application in drug development of mAbs. This set of mAbs was administered to both hemizygous and homozygous hFcRn transgenic mice (Tg32) at a single intravenous dose, and PK parameters were derived. Higher hFcRn protein tissue expression was confirmed by liquid chromatography-high resolution tandem mass spectrometry in Tg32 homozygous versus hemizygous mice. Clearance (CL) was calculated using non-compartmental analysis and correlations were assessed to historical data in wild-type mouse, non-human primate (NHP), and human. Results show that mAb CL in hFcRn Tg32 homozygous mouse correlate with human (r² = 0.83, r = 0.91, p < 0.01) better than NHP (r² = 0.67, r = 0.82, p < 0.01) for this dataset. Applying simple allometric scaling using an empirically derived best-fit exponent of 0.93 enabled the prediction of human CL from the Tg32 homozygous mouse within 2-fold error for 100% of mAbs tested. Implementing the Tg32 homozygous mouse model in discovery and preclinical drug development to predict human CL may result in an overall decreased usage of monkeys for PK studies, enhancement of the early selection of lead molecules, and ultimately a decrease in the time for a drug candidate to reach the clinic.     

New targets for an old drug

Methotrexate has been a clinical agent used in cancer, immunosuppression, rheumatoid arthritis and other highly proliferative diseases for many years, yet its underlying molecular mechanism of action in these therapeutic areas is still unclear. We present a chemical proteomics approach that uses ultra-sensitive mass spectrometry coupled to an inverse protein-ligand docking computational technique to unravel the mechanism of action of this drug. Using methotrexate tethered to a solid support we were able to isolate a signficant number of proteins. We effectively captured a large portion of the de novo purine metaolome and propose a pathway architecture similar to that seen in signaling pathways and consistent with substrate channeling. More importantly, we were able to capture protein targets that could potentially provide new insights into the mechanism of action of methotrexate in rheumatoid arthritis and immunosuppression. The application of this approach to other drugs and drug candidates may facilitate the prediction of unknown and secondary therapeutic target proteins and those related to the side effects and toxicity. These results demonstrate that this proteomics technology could play an important role in drug discovery and development since it allows monitoring of the interactions between a drug and the protein content of a cell.     

Method for evaluating metabolic functions of drugs in bioartificial liver

Lidocaine and galactose loading tests were performed on a bioartificial liver (BAL), an extracorporeal medical device incorporating living hepatocytes in a cartridge without a transport barrier across the membranes. The concentration changes were analyzed using pharmacokinetic equations to evaluate the efficacy and limitation of the proposed method. Lidocaine and galactose were found to be suitable drugs for a quantitative evaluation of the BAL functions, as they did not interact with the plasma proteins or blood vessels, making their concentrations easy to determine. The drug concentration changes after drug loading were easily analyzed using pharmacokinetic equations, and the BAL functions quantitatively expressed by pharmacokinetic parameters, such as the clearance (CL) and galactose elimination capacity (GEC). In addition, these two drugs have already been used in clinical tests to evaluate human liver functions over long periods, and lidocaineCL values andGEC values reported for a normal human liver. Thus, a comparison of theCL andGEC values for theBAL and a natural liver revealed what proportion of normal liver functions could be replaced by the BAL.     

Key factors influencing ADME properties of therapeutic proteins: A need for ADME characterization in drug discovery and development

Protein therapeutics represent a diverse array of biologics including antibodies, fusion proteins, and therapeutic replacement enzymes. Since their inception, they have revolutionized the treatment of a wide range of diseases including respiratory, vascular, autoimmune, inflammatory, infectious, and neurodegenerative diseases, as well as cancer. While in vivo pharmacokinetic, pharmacodynamic, and efficacy studies are routinely carried out for protein therapeutics, studies that identify key factors governing their absorption, distribution, metabolism, and excretion (ADME) properties have not been fully investigated. Thorough characterization and in-depth study of their ADME properties are critical in order to support drug discovery and development processes for the production of safer and more effective biotherapeutics. In this review, we discuss the main factors affecting the ADME characteristics of these large macromolecular therapies. We also give an overview of the current tools, technologies, and approaches available to investigate key factors that influence the ADME of recombinant biotherapeutic drugs, and demonstrate how ADME studies will facilitate their future development.     

In silico identification of common putative drug targets in <Emphasis Type="Italic">Leptospira interrogans</Emphasis>

Infectious diseases are the leading causes of death worldwide. Hence, there is a need to develop new antimicrobial agents. Traditional method of drug discovery is time consuming and yields a few drug targets with little intracellular information for guiding target selection. Thus, focus in drug development has been shifted to computational comparative genomics for identifying novel drug targets. Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. Availability of L. interrogans serovars and human genome sequences facilitated to search for novel drug targets using bioinformatics tools. The genome sequence of L. interrogans serovar Copenhageni has 5,124 genes while that of serovar Lai has 4,727 genes. Through subtractive genomic approach 218 genes in serovar Copenhageni and 158 genes in serovar Lai have been identified as putative drug targets. Comparative genomic approach had revealed that 88 drug targets were common to both the serovars. Pathway analysis using the Kyoto Encyclopaedia of Genes and Genomes revealed that 66 targets are enzymes and 22 are non-enzymes. Sixty two common drug targets were predicted to be localized in cytoplasm and 16 were surface proteins. The identified potential drug targets form a platform for further investigation in discovery of novel therapeutic compounds against Leptospira.     

Docking, virtual high throughput screening and in silico fragment-based drug design

The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomolecular target of therapeutical interest, the selection or the design of new lead compounds and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on molecular docking, virtual high-throughput screening and fragment-based ligand design.     

A quantitative model for metabolic intervention using gut microbes

As medicine shifts toward precision-based and personalized therapeutics, utilizing more complex biomolecules to treat increasingly difficult and rare conditions, microorganisms provide an avenue for realizing the production and processing necessary for novel drug pipelines. More so, probiotic microbes can be co-opted to deliver therapeutics by oral administration as living drugs, able to survive and safely transit the digestive tract. As living therapeutics are in their nascency, traditional pharmacokinetic–pharmacodynamic (PK–PD) models for evaluating drug candidates are not appropriate for this novel platform. Using a living therapeutic in late-stage clinical development for phenylketonuria (PKU) as a case study, we adapt traditional oral drug delivery models to properly evaluate and inform the engineering of living therapeutics. We develop the adapted for living therapeutics compartmental absorption and transit (ALT-CAT) model to provide metrics for drug efficacy across nine age groups of PKU patients and evaluate model parameters that are influenced by patient physiology, microbe selection and therapeutic production, and dosing formulations. In particular, the ALT-CAT model describes the mathematical framework to model the behavior of orally delivered engineered bacteria that act as living therapeutics by adapting similar methods that have been developed and widely-used for small molecular drug delivery and absorption.     

Biodiscovery from rare actinomycetes: an eco-taxonomical perspective

Microbial natural products, in particular, the ones produced by the members of the order Actinomycetales, will continue to represent an important route to the discovery of novel classes of bioactive compounds. As a result, the search for and discovery of lesser-known and/or novel actinomycetes is of significant interest to the industry due to a growing need for the development of new and potent therapeutic agents, mainly against drug resistant bacteria. Current advancements in genomics and metagenomics are adding strength to the target-directed search for detection and isolation of bioactive actinomycetes. New discoveries, however, will only stem from a sound understanding and interpretation of knowledge derived from conventional studies conducted since the discovery of streptomycin, on the ecology, taxonomy, physiology and metabolism of actinomycetes, and from a combination of this knowledge with currently available and continuously advancing molecular tools. Such a powerful information platform will then inevitably reveal the whereabouts, taxonomical and chemical identities of previously undetected bioactive actinomycetes including novel species of streptomycetes as potential producers of novel drug candidates.     

Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development

Effective translation of breakthrough discoveries into innovative products in the clinic requires proactive mitigation or elimination of several drug development challenges. These challenges can vary depending upon the type of drug molecule. In the case of therapeutic antibody candidates, a commonly encountered challenge is high viscosity of the concentrated antibody solutions. Concentration-dependent viscosity behaviors of mAbs and other biologic entities may depend on pairwise and higher-order intermolecular interactions, non-native aggregation, and concentration-dependent fluctuations of various antibody regions. This article reviews our current understanding of molecular origins of viscosity behaviors of antibody solutions. We discuss general strategies and guidelines to select low viscosity candidates or optimize lead candidates for lower viscosity at early drug discovery stages. Moreover, strategies for formulation optimization and excipient design are also presented for candidates already in advanced product development stages. Potential future directions for research in this field are also explored.     

Hologram QSAR model for the prediction of human oral bioavailability

A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure–activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q² = 0.70, r² = 0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.     

Addressing Central Nervous System (CNS) Penetration in Drug Discovery: Basics and Implications of the Evolving New Concept

Despite enormous efforts, achieving a safe and efficacious concentration profile in the brain remains one of the big challenges in central nervous system (CNS) drug discovery and development. Although there are multiple reasons, many failures are due to underestimating the complexity of the brain, also in terms of pharmacokinetics (PK). To this day, PK support of CNS drug discovery heavily relies on improving the blood–brain barrier (BBB) permeability in vitro and/or the brain/plasma ratio (K_p) in vivo, even though neither parameter can be reliably linked to pharmacodynamic (PD) and efficacy readouts. While increasing BBB permeability may shorten the onset of drug action, an increase in the total amount in brain may not necessarily increase the relevant drug concentration at the pharmacological target. Since the traditional K_p ratio is based on a crude homogenization of brain tissue, it ignores the compartmentalization of the brain and an increase favors non‐specific binding to brain lipids rather than free drug levels. To better link exposure/PK to efficacy/PD and to delineate key parameters, an integrated approach to CNS drug discovery is emerging which distinguishes total from unbound brain concentrations. As the complex nature of the brain requires different compartments to be considered when trying to understand and improve new compounds, several complementary parameters need to be measured in vitro and in vivo, and integrated into a coherent model of brain penetration and distribution. The new paradigm thus concentrates on finding drug candidates with the right balance between free fraction in plasma and brain, and between rate and extent of CNS penetration. Integrating this data into a coherent model of CNS distribution which can be linked to efficacy will allow it to design compounds with an optimal mix in physicochemical, pharmacologic, and pharmacokinetic properties, ultimately mitigating the risk for failures in the clinic.     

Establishing in vitro in vivo correlations to screen monoclonal antibodies for physicochemical properties related to favorable human pharmacokinetics

Implementation of in vitro assays that correlate with in vivo human pharmacokinetics (PK) would provide desirable preclinical tools for the early selection of therapeutic monoclonal antibody (mAb) candidates with minimal non-target-related PK risk. Use of these tools minimizes the likelihood that mAbs with unfavorable PK would be advanced into costly preclinical and clinical development. In total, 42 mAbs varying in isotype and soluble versus membrane targets were tested in in vitro and in vivo studies. MAb physicochemical properties were assessed by measuring non-specific interactions (DNA- and insulin-binding ELISA), self-association (affinity-capture self-interaction nanoparticle spectroscopy) and binding to matrix-immobilized human FcRn (surface plasmon resonance and column chromatography). The range of scores obtained from each in vitro assay trended well with in vivo clearance (CL) using both human FcRn transgenic (Tg32) mouse allometrically projected human CL and observed human CL, where mAbs with high in vitro scores resulted in rapid CL in vivo. Establishing a threshold value for mAb CL in human of 0.32 mL/hr/kg enabled refinement of thresholds for each in vitro assay parameter, and using a combinatorial triage approach enabled the successful differentiation of mAbs at high risk for rapid CL (unfavorable PK) from those with low risk (favorable PK), which allowed mAbs requiring further characterization to be identified. Correlating in vitro parameters with in vivo human CL resulted in a set of in vitro tools for use in early testing that would enable selection of mAbs with the greatest likelihood of success in the clinic, allowing costly late-stage failures related to an inadequate exposure profile, toxicity or lack of efficacy to be avoided.     

Novel series of 1,2,4-trioxane derivatives as antimalarial agents

Among three series of 1,2,4-trioxane derivatives, five compounds showed good in vitro antimalarial activity, three compounds of which exhibited better activity against P. falciparum resistant (RKL9) strain than the sensitive (3D7) one. Two best compounds were one from aryl series and the other from heteroaryl series with IC₅₀ values of 1.24 µM and 1.24 µM and 1.06 µM and 1.17 µM, against sensitive and resistant strains, respectively. Further, trioxane derivatives exhibited good binding affinity for the P. falciparum cysteine protease falcipain 2 receptor (PDB id: 3BPF) with well defined drug-like and pharmacokinetic properties based on Lipinski’s rule of five with additional physicochemical and ADMET parameters. In view of having antimalarial potential, 1,2,4-trioxane derivative(s) reported herein may be useful as novel antimalarial lead(s) in the discovery and development of future antimalarial drug candidates as P. falciparum falcipain 2 inhibitors against resistant malaria.     

High-throughput human metabolism and toxicity analysis

Poor drug candidate safety profiles are often identified late in the drug development process, manifesting themselves in the preclinical and clinical phases and significantly contributing to the high cost and low yield of drug discovery. As a result, new tools are needed to accelerate the assessment of drug candidate toxicity and human metabolism earlier in the drug development process, from primary drug candidate screening to lead optimization. Although high-throughput screens exist for much of the discovery phase of drug development, translating such screening techniques into platforms that can accurately mimic the human in vivo response and predict the impact of drug candidates on human toxicology has proven difficult. Nevertheless, some success has been achieved in recent years, which may ultimately yield widespread acceptance in the pharmaceutical industry.     

Inhibitors of CPH1-MAP Kinase Pathway: Ascertaining Potential Ligands as Multi-Target Drug Candidate in Candida albicans

A number of proteins contributing in pathogen’s virulence mechanisms offer a potential target for anticandidal therapeutics. CPH1 and its regulatory proteins Cst20, Hst7, Cek1 (MAPK cascade) administers filamentation and morphogenesis in human pathogenic fungus Candida albicans. These proteins are essential targets for their involvement in the successful establishment of the fungi within the host. In silico drug design using virtual screening, docking and (ADME)/Tox analysis for identification of lead compounds is an economic strategy for the development of potent anticandidal agent. The study divulged five persuasive ligands (2-O-prenyl coumaric acid, 2-nitro-4-methyl-cinnamaldehyde, 3,5-diprenyl-4-coumaric acid, VT1161, T-2307) out of 25, which collectively inhibited Cst20, Hst7, Cek1 in C. albicans. They can hence be used as natural drug leads for designing more effective inhibitors of multiple targets for C. albicans survival and progression of the disease. This study will enhance our understanding of the phenomenon “multiple targeting” and multi-target drug discovery further accelerating efficient broad-spectrum antifungal therapeutics development in near future. This study provides a good platform to eradicate the issue of “target shortage” that might facilitate the discovery of novel drugs in near future because the prolonged use of antibiotics over the years has transformed pathogenic fungus into resistant to many drugs.