Prediction of active human dose: learnings from 20 years of Merck KGaA experience, illustrated by case studies

Use of pharmacokinetic versus pharmacodynamic endpoints to support human dose predictions: implications for rational drug design and early clinical development

INTRODUCTION

The predicted human dose regimen of new chemical entities represents the most holistic and clinically relevant measure of drug-likeness upon which to base decisions in drug design and selection of candidate molecules for further development. Likewise, the predicted human dose regimen for efficacy and safety provides critical insight into clinical development planning. As such, human dose predictions are commonly generated in early stages of research and continually revisited as new data are generated through development.

AREAS COVERED

In this work, the authors illustrate scenarios where conventional approaches based on discrete pharmacokinetic metrics are inappropriate and propose a generalizable approach leveraging a predicted average pharmacodynamic effect rather than pharmacokinetic metrics. Preclinical and clinical data of a JAK inhibitor, tofacitinib, were used to illustrate the relative value of this approach to human dose prediction.

EXPERT OPINION

Due to the simplicity of implementation, pharmacokinetic-based approaches which target a discrete maximal, average, or minimum concentration have been widely used across the pharmaceutical industry. However, in emphasizing only one point on the overall exposure-time profile, such approaches can be misleading in terms of the expected pharmacodynamic effect. For future projections, the authors recommend using the average pharmacodynamic effect-based approach to calculate human efficacious dose.

Bridging the gap: From petri dish to patient - Advancements in translational drug discovery

Translational research serves as the bridge between basic research and practical applications in clinical settings. The journey from "bench to bedside" is fraught with challenges and complexities such as the often-observed disparity between how compounds behave in a laboratory setting versus in the complex systems of living organisms. The challenge is further compounded by the limited ability of in vitro models to mimic the specific biochemical environment of human tissues. This article explores and details the recent advancements and innovative approaches that are increasingly successful in bridging the gap between laboratory research and patient care. These advancements include, but are not limited to, sophisticated in vitro models such as organ-on-a-chip and computational models that utilize artificial intelligence to predict drug efficacy and safety. The article aims to showcase how these technologies improve the predictability of drug performance in human bodies and significantly speed up the drug development process. Furthermore, it discusses the role of biomarker discovery in preparation of more targeted and personalized therapy approaches and covers the impact of regulatory changes designed to facilitate drug approvals. Additionally, by providing detailed case studies of successful applications, we illustrate the practical impacts of these innovations on drug discovery and patient care.

Application of Mechanistic Multiparameter Optimization and Large-Scale In Vitro to In Vivo Pharmacokinetics Correlations to Small-Molecule Therapeutic Projects

Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g., minimizing dose, maximizing safety margins, and/or minimizing drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo pharmacokinetic (PK) properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small-molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top second percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared with the rest of the compounds being synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.

Technology Readiness Level Roadmap for Developing Innovative Herbal Medicinal Products

Despite the vast global botanical diversity, the pharmaceutical development of herbal medicinal products (HMPs) remains underexploited. Of over 370,000 described plant species, only a few hundred are utilized in HMPs. Most of these have originated from traditional use, and only a minority come from megadiverse countries. Exploiting the pharmacological synergies of the hundreds of compounds found in poorly studied plant species may unlock new therapeutic possibilities, enhance megadiverse countries' scientific and socio-economic development, and help conserve biodiversity. However, extensive constraints in the development process of HMPs pose significant barriers to transforming this unsatisfactory socio-economic landscape. This paper proposes a roadmap to overcome these challenges, based on the technology readiness levels (TRLs) introduced by NASA to assess the maturity of technologies. It aims to assist research entities, manufacturers, and funding agencies from megadiverse countries in the discovery, development, and global market authorization of innovative HMPs that comply with regulatory standards from ANVISA, EMA, and FDA, as well as WHO and ICH guidelines.

Human Dose and Pharmacokinetic Predictions for Biologics at Boehringer Ingelheim: A Retrospective Analysis

INTRODUCTION

Accurate predictions of pharmacokinetics and efficacious doses for biologics in humans are critical for selecting appropriate first-in-human starting doses and dose ranges and for estimating clinical material needs and cost of goods. This also impacts clinical feasibility, particularly for subcutaneously administered biologics.

METHODS

We performed a comprehensive comparison between predicted and observed clearances and doses in humans for a set of 22 biologic drugs developed at Boehringer Ingelheim (BI) over the last 2 decades. The analysis included biologics across three therapeutic areas comprising a wide variety of modalities: mono- and bispecific monoclonal antibodies (mAbs) and nanobodies and a Fab fragment.

RESULTS

Our analysis showed that observed clearances in humans were within twofold of predicted clearances for 17 out of 20 biologics (85%). Six biologics had uncharacteristically high observed human clearances (range 32-280 mL/h) for their respective molecular classes, impacting their clinical developability. For three molecules, molecular characteristics contributed to the high clearance. Clinically selected doses were within twofold of predicted for 58% of projects. With 42% and 25% of projects selecting clinical doses higher than two- or threefold the predicted value, respectively, the importance of better understanding not only the pharmacokinetic (PK) but also the predictivity of pharmacodynamic models is highlighted.

CONCLUSIONS

We provide a clinical pharmacology perspective on the commonly accepted twofold range of human clearance predictions as well as the implications of higher than predicted targeted efficacious plasma concentration on clinical development. Finally, an analysis of key success factors for biologics at BI was conducted, which may be relevant for the entire pharmaceutical industry. This is one of the largest retrospective analyses for biologics and provides further evidence that successful predictions of human PK and efficacious dose will be further facilitated by gathering key translational data early in research.

Towards holistic Compound Quality Scores: Extending ligand efficiency indices with compound pharmacokinetic characteristics

The suitability of small molecules as oral drugs is often assessed by simple physicochemical rules, the application of ligand efficiency scores or by composite scores based on physicochemical compound properties. These rules and scores are empirical and typically lack mechanistic background, such as information on pharmacokinetics (PK). We introduce new types of Compound Quality Scores (CQS, specifically called dose scores and cmax scores), which explicitly include predicted or, when available, experimental PK parameters and combine these with on-target potency. These CQS scores are surrogates for an estimated dose and corresponding cmax and allow prioritizing of compounds within test cascades as well as before synthesis. We demonstrate the complementarity and, in most cases, superior performance relative to existing efficiency metrics by project examples.

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

The quantitative prediction of human pharmacokinetics (PK) including the PK profile and key PK parameters are critical for early drug development decisions, successful phase I clinical trials, and the establishment of a range of doses to enable phase II clinical dose selection. Here, we describe an approach employing physiologically based pharmacokinetic (PBPK) modeling (Simcyp) to predict human PK and to validate its performance through retrospective analysis of 18 Genentech compounds for which clinical data are available. In short, physicochemical parameters and in vitro data for preclinical species were integrated using PBPK modeling to predict the in vivo PK observed in mouse, rat, dog, and cynomolgus monkey. Through this process, the in vitro to in vivo extrapolation (IVIVE) was determined and then incorporated into PBPK modeling in order to predict human PK. Overall, the prediction obtained using this PBPK-IVIVE approach captured the observed human PK profiles of the compounds from the dataset well. The predicted Cmax was within 2-fold of the observed Cmax for 94% of the compounds while the predicted area under the curve (AUC) was within 2-fold of the observed AUC for 72% of the compounds. Additionally, important IVIVE trends were revealed through this investigation, including application of scaling factors determined from preclinical IVIVE to human PK prediction for each molecule. Based upon the analysis, this PBPK-based approach now serves as a practical strategy for human PK prediction at the candidate selection stage at Genentech.

Utility of preclinical species for uncertainty assessment and correction of prediction of human volume of distribution using the Rodgers-Lukacova model

Prediction of rat, dog, monkey, and human volume of distribution (VD_ss) by Rodgers-Lukacova model was evaluated using a data set of more than 100 compounds.The prediction accuracy was best for humans followed by monkeys and dogs with 59, 52, and 41% of compounds within 2-fold, respectively.The accuracy of predictions in preclinical species was indicative of the human situation. This was particularly true for monkeys, where 87% of the compounds that were predicted within 2-fold in monkeys were also predicted within 2-fold in humans.The model's tendency to underestimate VD_ss was higher in rats and dogs compared to humans and monkeys for all ion classes but zwitterions. Hence, correction of human predictions using prediction errors in rats and dogs resulted in overestimation of VD_ss.The model had a similar degree of underestimation in humans and monkeys. Correction using monkeys improved the accuracy of the human estimate, especially for basic and zwitterion compounds.A strategy is proposed based on the accuracy of prediction in monkey and monkey scalars for prediction and prospective assessment of the accuracy of human VD_ss.

Development of novel ultra-sensitive IL-11 target engagement assays to support mechanistic PK/PD modeling for an anti-IL-11 antibody therapeutic

An in-house antibody generation campaign identified a diverse, high affinity set of anti-interleukin-11 (IL-11) monoclonal antibodies (mAbs) to enable successful development of novel, custom ultra-sensitive target engagement assays for detection of “free” (unbound to the dosed anti-IL-11 therapeutic mAb) and “total” (free and mAb-IL-11 complexed form) IL-11 in preclinical species and human. Antibody hits from distinct epitope communities were screened on various platforms, including enzyme-linked immunosorbent assay, Meso Scale Discovery, Simoa HD-1 and Simoa Planar Array (SP-X), and used for assay development and sensitivity optimization. The ultra-sensitive SP-X format achieved a lower limit of quantitation of 0.006 pg/mL, enabling the first reported baseline levels of IL-11 in healthy control plasma determined by custom bioanalytical assays. These newly established baseline levels supported mechanistic pharmacokinetic/pharmacodynamic modeling in mouse, cynomolgus monkey, and human for a greater understanding of preclinical study design and in vivo dynamic interaction of soluble IL-11 with an anti-IL-11 antibody therapeutic candidate. Modeling and simulation also helped refine the utility of assays with respect to their potential use as target engagement biomarkers in the clinic.

Abbreviations IL-11: Interleukin-11, TE: Target engagement, PK/PD: Pharmacokinetic/pharmacodynamic, mAb: Monoclonal antibody, NHP: Non-human primate, IgG: Immunoglobulin G, Cyno: Cynomolgulus monkey, GFR: Glomerular filtration rate, BQL: Below quantitation levels, DRM: Disease relevant model, kDa: kilodaltons, SPR: Surface plasmon resonance, pSTAT3: phosphorylated STAT3, IL-11R: Interleukin-11 receptor, TPP: Target product protein, LLOQ: Lower limit of quantitation, RLU: Relative light units

Molecular Characteristics of Repotrectinib That Enable Potent Inhibition of TRK Fusion Proteins and Resistant Mutations

NTRK chromosomal rearrangements yield oncogenic TRK fusion proteins that are sensitive to TRK inhibitors (larotrectinib and entrectinib) but often mutate, limiting the durability of response for NTRK ⁺ patients. Next-generation inhibitors with compact macrocyclic structures (repotrectinib and selitrectinib) were designed to avoid resistance mutations. Head-to-head potency comparisons of TRK inhibitors and molecular characterization of binding interactions are incomplete, obscuring a detailed understanding of how molecular characteristics translate to potency. Larotrectinib, entrectinib, selitrectinib, and repotrectinib were characterized using cellular models of wild-type TRKA/B/C fusions and resistance mutant variants with a subset evaluated in xenograft tumor models. Crystal structures were determined for repotrectinib bound to TRKA (wild-type, solvent-front mutant). TKI-naïve and pretreated case studies are presented. Repotrectinib was the most potent inhibitor of wild-type TRKA/B/C fusions and was more potent than selitrectinib against all tested resistance mutations, underscoring the importance of distinct features of the macrocycle structures. Cocrystal structures of repotrectinib with wild-type TRKA and the TRKA^G595R SFM variant elucidated how differences in macrocyclic inhibitor structure, binding orientation, and conformational flexibility affect potency and mutant selectivity. The SFM crystal structure revealed an unexpected intramolecular arginine sidechain interaction. Repotrectinib caused tumor regression in LMNA-NTRK1 xenograft models harboring GKM, SFM, xDFG, and GKM + SFM compound mutations. Durable responses were observed in TKI-naïve and -pretreated patients with NTRK ⁺ cancers treated with repotrectinib (NCT03093116). This comprehensive analysis of first- and second-generation TRK inhibitors informs the clinical utility, structural determinants of inhibitor potency, and design of new generations of macrocyclic inhibitors.

Harnessing the predictive power of preclinical models for oncology drug development

Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.

TPX-0131, a Potent CNS-penetrant, Next-generation Inhibitor of Wild-type ALK and ALK-resistant Mutations

Since 2011, with the approval of crizotinib and subsequent approval of four additional targeted therapies, anaplastic lymphoma kinase (ALK) inhibitors have become important treatments for a subset of patients with lung cancer. Each generation of ALK inhibitor showed improvements in terms of central nervous system (CNS) penetration and potency against wild-type (WT) ALK, yet a key continued limitation is their susceptibility to resistance from ALK active-site mutations. The solvent front mutation (G1202R) and gatekeeper mutation (L1196M) are major resistance mechanisms to the first two generations of inhibitors while patients treated with the third-generation ALK inhibitor lorlatinib often experience progressive disease with multiple mutations on the same allele (mutations in cis, compound mutations). TPX-0131 is a compact macrocyclic molecule designed to fit within the ATP-binding boundary to inhibit ALK fusion proteins. In cellular assays, TPX-0131 was more potent than all five approved ALK inhibitors against WT ALK and many types of ALK resistance mutations, e.g., G1202R, L1196M, and compound mutations. In biochemical assays, TPX-0131 potently inhibited (IC50 <10 nmol/L) WT ALK and 26 ALK mutants (single and compound mutations). TPX-0131, but not lorlatinib, caused complete tumor regression in ALK (G1202R) and ALK compound mutation-dependent xenograft models. Following repeat oral administration of TPX-0131 to rats, brain levels of TPX-0131 were approximately 66% of those observed in plasma. Taken together, preclinical studies show that TPX-0131 is a CNS-penetrant, next-generation ALK inhibitor that has potency against WT ALK and a spectrum of acquired resistance mutations, especially the G1202R solvent front mutation and compound mutations, for which there are currently no effective therapies.

Indole Inhibitors of MMP-13 for Arthritic Disorders

Here, we described the design, by fragment merging and multiparameter optimization, of selective MMP-13 inhibitors that display an appropriate balance of potency and physicochemical properties to qualify as tool compounds suitable for in vivo testing. Optimization of potency was guided by structure-based insights, specifically to replace an ester moiety and introduce polar directional hydrogen bonding interactions in the core of the molecule. By introducing polar enthalpic interactions in this series of inhibitors, the overall beneficial physicochemical properties were maintained. These physicochemical properties translated to excellent drug-like properties beyond potency. In a murine model of rheumatoid arthritis, treatment of mice with selective inhibitors of MMP-13 resulted in a statistically significant reduction in the mean arthritic score vs control when dosed over a 14 day period.