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Improving the odds of success in drug discovery: Choosing the best compounds for in vivo toxicology studies Travis T Wager, Bethany Kormos, Joseph T. Brady, Yvonne Will, Michael Aleo, Donald Stedman, Max Kuhn, and ramalakshmi chandrasekaran J. Med. Chem., Just Accepted Manuscript • Publication Date (Web): 12 Nov 2013 Downloaded from http://pubs.acs.org on November 13, 2013

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Journal of Medicinal Chemistry

Improving the odds of success in drug discovery: Choosing the best compounds for in vivo toxicology studies Travis T. Wager,*, Bethany L. Kormos, Joseph T. Brady, Yvonne Will, Michael D. Aleo, Donald B. Stedman, Max Kuhn and Ramalakshmi Y. Chandrasekaran

Pfizer Worldwide Research and Development, 700 Main Street, Cambridge, MA 02139 (TTW, BLK); Groton Laboratories, Eastern Point Road, Groton, Connecticut 06340 (JTB, YW, MDA, DBS, MK, RYC).

ABSTRACT:

A set of molecules that advanced into exploratory animal toxicology studies (two species) was examined to determine what properties contributed to success in these safety studies. Compounds were rigorously evaluated across numerous safety endpoints and classified as “pass” if a suitable in vivo therapeutic index (TI) was achieved for advancement into regulatory toxicology studies. The most predictive endpoint contributing to compound survival was a predicted human efficacious concentration (Ceff) of ≤250 nM (total drug) and ≤40 nM (free drug). This trend held across a wide range of CNS modes of action, encompassing targets such as enzymes, G protein-coupled receptors, ion channels and transporters.

Introduction The relationship between physicochemical properties and drug attributes has received significant attention for over a decade.1-14 The goal of these efforts has been to improve early 1 ACS Paragon Plus Environment


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drug discovery efficiencies and increase survival rates of drug candidates. Several groups have analyzed various data sets and generated volumes of new data to allow a direct comparison of properties of compounds versus an assortment of endpoints. Hughes et al. examined a set of 245 compounds, evaluating the relationship between physicochemical properties and in vivo toxicity.12 From this work a useful relationship emerged between toxicity and two physicochemical properties: compounds with high lipophilicity (ClogP > 3) and low polar surface area (TPSA < 75) had an increased risk of generalized toxicities in short-term animal studies. In 2010, we analyzed a set of marketed CNS drugs and clinical CNS candidates to establish optimal physicochemical, ADME (absorption, distribution, metabolism, and excretion), ligand efficiency and safety properties.14

This work resulted in the construction and

implementation of a prospective design tool (CNS MPO) that focused holistically on alignment of drug properties in a single molecule.15 Considerable effort has been invested in understanding the relationship between in vitro potency, molecular mass, lipophilicity and optimal drug space.10,

14, 16, 17

However, to our

knowledge, none of these analyses have incorporated in vivo-derived predicted human efficacious concentrations (Ceff) into their analyses, despite the fact that it is one of the two values used in determining the therapeutic index (TI), a key factor in compound progression. Further, lack of efficacy contributes significantly to drug candidate attrition, accounting for approximately 30% of clinical failures in 2000.18 There are many potential reasons for lack of efficacy in the clinic; inadequate drug exposure at the target should not be one of them. In order for clinical scientists to fully test a hypothesis in the clinic, compounds with appropriate and sufficient TIs need to be utilized, so that the full target occupancy range can be evaluated. Incorporating in vivo-derived Ceff into analyses aimed at understanding optimal drug properties and early attrition, especially safety attrition, represents an enormous opportunity to increase the survival of drug candidates. 2 ACS Paragon Plus Environment

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Establishing an in vivo No Observable Adverse Effect Level (NOAEL) is a requirement to open an Investigational New Drug application (IND) and a suitable TI is must be achieve so that the clinical hypothesis can be tested.19 To establish a TI, both the Ceff and the in vivo NOAEL concentration must be determined; the ratio between the in vivo NOAEL concentration and Ceff yields the TI of the compound (Equation 1). Where Ceff is typically the predicted human efficacious drug concentration at steady state (Css,ave 0-24h). Acceptable TIs are dependent on the type of toxicity observed, duration of treatment, and patient population. These factors make it difficult to establish absolute go/no-go TI criteria and result in there being little literature or published regulatory guidance on TI.19, 20 Analysis of a set of compounds that has completed exploratory toxicology studies (ETS), each of which has been categorized as “pass” or “fail” using a TI criterion, should provide significant benefit to the drug discovery community and perhaps further improve drug development cycle time.

!" # #

Therapeutic Index TI $%!"

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(1)

Within this body of work we have examined the relationship between molecular and ADME properties, in vitro potency and predicted clinical efficacious concentration (Ceff) versus exploratory pass/fail toxicology outcomes defined by TI. The goal of this work was to establish a set of principles, beyond traditional endpoints, to aid in the prospective selection of exploratory toxicology study compounds, with the goal of achieving a higher probability of success in these in vivo safety studies and improving survival odds.

Results and Discussion The exploratory toxicology study (ETS) compound set consisted of 56 compounds from 15 different mechanisms of action (MOA). The MOA(s) encompass a diverse set of biological 3 ACS Paragon Plus Environment


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targets, including enzymes, GPCRs, ion channels, and transporters (Figure 1). Where possible, each MOA had at least two compounds, one or more of which cleared toxicity studies in two species (rodent and non-rodent). Physicochemical properties, ADME experimental values, and in vitro and in vivo potencies were compiled and rigorously analyzed to develop a better understanding of the factors that influence ETS outcomes, beyond the traditional parameters of lipophilicity, polarity and selectivity. Compounds for each MOA were categorized as ETS pass or fail, based on the TI calculated from the ratio of highest concentration of drug in vivo that resulted in no observable adverse toxicity to the concentration predicted to produce the desired biological response in humans. Compounds with suitable TIs were classified as “pass” and those without acceptable safety margins as “fail”. While no absolute guideline for TIs exist (or should exist, given the complex nature of toxicity, efficacy, and diseases being treated), for the purposes of this work we used a general NOAEL TI pass/fail cut-off criterion of 30 (Cmax was used for seizures and Cave for histopathology). When the first species toxicity study resulted in an unacceptable TI, no second species data is available. The ETS compounds within this analysis were originally intended as oral, centrally-acting drugs for CNS applications; however, this set of compounds generally represents other therapeutic areas due to the broad range of targets (kinase, phosphodiesterase, protease, transferase, GPCRs, ion channel and transporters) employed, all of which are expressed throughout the human body. Significantly, examination of the attrition toxicity revealed that only four compounds failed due to CNS toxicity (seizures). This is not surprising, as toxicity, which may be caused by on-target/off-target pharmacology, often occurs in tissues other than those targeted for drug efficacy.

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Figure 1. Mechanisms of action (MOA) for the compound set, binned into primary and target family. Numbers represent compounds evaluated in an exploratory toxicology study (ETS). Compounds were classified as pass (green) or fail (red) according to their ETS result. Where data is available from two species, a pass/fail assessment was made using both sets of outcomes. For example, two pass outcomes yields an overall pass, whereas one pass (rat) and one fail (second species) yields a definitive fail. All pass and fail outcomes were consistently determined using the TI. Examination of ETS outcomes in ClogP and TPSA property space as defined by Hughes et al.12 revealed that both pass and fail compounds occupied all four quadrants, with little bias towards favorable property space for the compounds that passed ETS (Figure 2). While several compounds passed ETS in the space of lower safety risk (ClogP < 3 and TPSA > 75), significant compound attrition is still encountered in this property space. Further, several compounds survived ETS despite occupying property space of highest safety risk (ClogP > 3 and TPSA < 75).

Hughes et al.12 defined toxicity independent of TI, which may explain the low ETS

outcome predictability for this set using ClogP and TPSA. Examination of the ETS compound 5 ACS Paragon Plus Environment


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set based solely on rat ETS NOAEL concentration revealed that 19% of the pass compounds survived rat ETSs despite a low NOAEL of ≤1 µM total drug (6/31) and conversely a significant number of compounds failed ETS even though they had NOAEL concentration > 10 µM total drug (7/22 failures). This observation is not unexpected: compounds with low NOAEL can achieve an acceptable TI if the projected Ceff is low, while compounds will fail ETSs if their Ceff is too high relative to their NOAEL.

Figure 2. Distribution of exploratory toxicology compounds in the ClogP and TPSA property space. Black lines represent the cutoff values for preferred ClogP (75) as established by Hughes et al.12 Compounds are colored by ETS outcome: compounds that passed ETS are in green and failed compounds are in red.


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It is common practice in the pharmaceutical industry to conduct an ETS on lead candidates before the Good Laboratory Practice (GLP) toxicology studies, which are required by the FDA in two species prior to first-in-human studies. Compounds frequently fail these ETS studies due to inadequate TI in rodent or non-rodent species; if we could prospectively select compounds to enter safety studies that had a higher probability of a favorable outcome, we could both decrease animal usage and potentially reduce the time it takes to identify an acceptable clinical candidate, given the expensive and time-consuming nature of these in vivo toxicity tests. For these reasons we examined the relationship between ETS outcomes (pass/fail) and key compound characteristics: in vitro potency, descriptors that correlate potency, molecular mass, and lipophilicity (LE, LipE and LELP), and Ceff.15, 16, 21, 22 Large ranges in values for in vitro potency and potency efficiencies (LE, LipE, and LELP) exist for compounds that pass ETS (Figure 3). No absolute guideline could be established for in vitro potency or the various potency efficiencies; however, there was a relative relationship within MOA. Consistent with historical perceptions, compounds that were the most potent in vitro and had the best efficiencies (high LE, high LipE and low LELP) were more likely to survive ETS for that MOA. While very useful in structure-activity relationships (SAR), this fact is less helpful in ascertaining when a compound is potent enough to enter an early safety study. Perhaps the inability to identify an absolute in vitro value that predicts ETS survival with high probability is a result of the fact that in vitro potency serves only as a surrogate for in vivo potency, and fails to recapitulate the whole biological system. We then examined the relationship between ETS pass/fail and Ceff for this compound set, and identified statistically significant absolute Ceff values of 250 nM (total drug) (p < 0.0001) and 40 nM (free drug) (p = 0.0532) (Figure 4). While both total and free Ceff analyses were statistically significant, the 250 nM (total drug) concentration survival guideline had a better positive predictive value than did the free drug level (18% higher). In addition, the compound 7 ACS Paragon Plus Environment


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with the lowest Ceff value within a given MOA was more frequently the compound that survived ETS in two species and progressed to regulatory toxicology studies. Collectively, this suggests that an increase in ETS survival odds may be achieved if compounds are prospectively selected for ETS using an absolute value of ≤250 nM Ceff (total drug), ≤40 nM Ceff (free drug) and lowest Ceff within a particular MOA.

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Figure 3. Distribution of exploratory toxicology compounds within mechanism of actions (MOA, Figure 1), colored by ETS outcome: compounds that passed ETS are in green and failed compounds are in red. A) in vitro potency (Ki, IC50, EC50, Kinact/Ki); B) Ligand efficiency (LE); C) Lipophilic efficiency (LipE); D) Ligand-efficiency-dependent lipophilicity (LELP). While we chose to include the data for the irreversible inhibitors targeting MOA 6 in the plots this mode of actions, in vitro potency, LE, LipE and LELP cannot directly compared to other targets.


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Figure 4.

Distribution of exploratory toxicology compounds within mechanisms of action

(MOA) colored by ETS outcome: compounds that passed ETS are in green and failed compounds are in red. A) Ceff (total drug) compound distribution, log plot and pie graph binned values. B) Ceff (free drug) compound distribution, log plot and pie graph binned values. For each MOA, the best compound was examined to determine if it passed an ETS (yes or no) and MOA with only one outcome were catorgized as not applicable (n/a).

We also examined the collective Ceff total and free drug data set using a receiver operating characteristic (ROC) plot (Figure 5A).23, 24 The ROC curve is a mathematical tool that can be used to characterize the effectiveness of a potential predictor for a binary outcome.23, 24 The curve is generated from sensitivity and specificity values evaluated over a continuum of 9 ACS Paragon Plus Environment


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cutoff values. The ROC curve plots sensitivity (i.e., true positive rate) versus one minus the specificity (i.e., the false positive rate) using the results of each cutoff for the predictor. Using this curve, the tradeoff between sensitivity and specificity can be evaluated and an appropriate cutoff can be determined based on the context of the problem (e.g., high sensitivity). Visual inspection of the data with the goal of maximizing ETS pass compounds once again yielded a Ceff (total drug) equal to or less than 250 nM, which provides high specificity (0.96) and moderate sensitivity (0.58). Utilizing the ROC plot, we identified the Ceff (total drug) value for both optimal specificity and sensitivity to be 119 nM, which provides a specificity and sensitivity of 0.78 and 0.70, respectively. The same was done for free drug levels, where a 40 nM cutoff yielded sensitivity and specificity of 0.78 and 0.48, respectively, and the optimal specificity and sensitivity value was 16 nM (0.74, 0.73). One additional metric used to summarize the ROC curve is area under the curve (AUC).

In the best case, where the predictor perfectly

differentiates the two potential outcomes, the AUC would be one. The ROC curve for a noninformative predictor would follow a diagonal 45 degree line and result in an AUC of approximately 0.50. One approach to comparing such AUC values is via hypothesis testing using the method of DeLong.25 The ROC AUCs for both total and free drug show good predictivity (0.775 and 0.711, respectively) and both parameters have similar predictability when evaluating across the full drug exposure data range. Leveraging the data, we built a simple logistic regression model to define the probability of success across the exposure range achieved in this data set for both free and total drug (Figure 5B).26 As the Ceff decreases, the probability of success increases. For example, a compound with Ceff (total drug) of 1 nM has a >80% probability of success versus only 20% at 1 µM. An advantage of the logistic regression model is that it avoids hard cutoffs and provides a prospective rough probability of success for compounds at a given efficacious concentration.


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Figure 5. A) Receiver operating characteristic (ROC) curves for both total and free drug concentration. Purple represents total drug and orange represents free drug. B) Logistic regression model relating Ceff to the probability of ETS success.26 The hash marks on the upper axis of the plot indicate ETS pass (green) or failure (red) for Ceff (total drug). The corresponding hash marks on the bottom of the graph are the analogous data for Ceff (free drug).

Previously, we disclosed a prospective design tool (CNS MPO) that focused on aligning drug attributes within single molecules.15 The physicochemical property-based (ClogP, TPSA, ClogD, MM, HBD, pKa) scoring provides a tool for rapid assessment of new molecule design ideas. The higher the CNS MPO desirability score, the higher the probability of alignment of drug attributes: permeability (Papp), P-glycoprotein (P-gp) efflux, and metabolic stability (CLint). Using this tool, we examined whether an increasing CNS MPO score resulted in an increase in the alignment of drug attributes for this set of molecules as well (Supplementary Figure 1s). In the event, over 95% of the compounds with a CNS MPO score ≥5 possessed full alignment. We next paired the CNS MPO design tool with the newly established ETS Ceff (total drug) selection criteria to integrate efficacy with ADME and physicochemical drug attributes, in the expectation 11 ACS Paragon Plus Environment


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of establishing improved principles that would enhance compound quality and improve survival rates. Indeed, compounds that possessed the triad of low Ceff, alignment of ADME properties and high CNS MPO desirability scores had the highest odds of survival (Figure 6). Of the set of compounds that successfully progressed through safety studies, 67% (14/21 compounds) had full alignment of Ceff, ADME and physicochemical properties. In the set of compounds that failed safety studies, 77% (24/31 compounds) failed to fully align drug attributes, with efficacy having the most prevalent mis-alignment at 55% (17/31 compounds). This suggests that part of the safety failure of these compounds can be attributed to suboptimal in vivo potency, because of the corresponding dimunution of TI.

Figure 6. Venn wedge diagram of binned values for alignment of desired attributes: Ceff (total drug level); alignment of desired ADME attributes: high Papp, low P-gp, and low CLint,u; and optimal physicochemical properties (CNS MPO score ≥ 4). The number of compounds in each pie is given within the pie graph. Four compounds did not have all endpoints and thus were not included in this diagram.

Conclusion


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Attrition of drug candidates is multi-factorial and thus a holistic strategy that addresses key attrition factors (safety, ADME, and efficacy) is required if an improvement in survival rates through Phase III is to be achieved. The prospective design tool “CNS MPO” can enable the alignment of ADME attributes (CLint, Papp, and P-gp), but it is only part of a potential solution. As part of our effort to build a comprehensive drug design strategy, we continue to investigate ways to improve the probability of survival for drug candidates. We discovered that integrating measures of physicochemical properties, ADME, and efficacy (Ceff) can enhance in vivo safety success; underpinning this finding was the integration of TIs into the analysis. The therapeutic index combines safety and efficacy into a single value that has scientific utility, and is a key component of success or failure in ETS studies. Given the importance of Ceff in these go/no-go decisions, investing the appropriate amount of time to robustly define this value beyond in vitro systems will yield higher success rates not only in toxicology studies but also in the launch of new medicines. From our work we determined that, for this set of compounds, each MOA has a unique in vitro, LE, LipE and LELP distribution and no absolute value could be established to discriminate between ETS pass/fail compounds. In vitro potency can guide SAR; however, due diligence in establishing a robust in vivo efficacy value is required to fully understand how a compound responds in a fully functioning biological system. A clear and statistically significant Ceff survival value was established using this set of compounds:

projected human Ceff (total drug)

and Ceff (free drug) values of ≤250 nM and ≤40 nM, respectively, correlated with higher TIs and higher in vivo safety survival rates. A simple linear regression model that describes a continuum of probability of success based on Ceff may enable better decision-making in the context of ETS compound selection. The most desirable compounds with the highest survival odds possessed alignment of lower Ceff and higher desirability CNS MPO scores, which incorporate both physicochemical property and ADME attributes. A 2 x 2 matrix constructed using binned CNS 13 ACS Paragon Plus Environment


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MPO desirability scores and Ceff for both total (≤250 nM) and free (≤40 nM) can be used to summarize our findings (Table 1); a scatter plot of the individual compounds can be found in the supplementary material, Figure 2s. Compounds with Ceff ≤ 250 nM (total drug) and CNS MPO ≥ 4 are approximately 2 times more likely to pass than fail exploratory toxicology studies, whereas compounds with higher Ceff values (>250 nM) were highly unlikely to pass safety studies (odds of 0.06). Odds ratios using a conditional maximum likelihood estimator (CMLE) are 29.5 and 3.4 for total and free drug levels, respectively.27

Table 1. Observed odds for ETS Pass versus Ceff, for compounds with CNS MPO > 4 Total Drug ETS Outcome (Pass/Fail) CNS MPO < 4 CNS MPO > 4 Ceff >250

NA (0/2)

0.059 (1/17)

Ceff ≤ 250

1 (5/5)

1.9 (17/9) a

Odds ratio, CMLE = 29.5 (3.56, 1410.00) a

Free Drug ETS Outcome (Pass/Fail) CNS MPO < 4 CNS MPO > 4 Ceff >40

NA (0/1)

0.33 (5/15)

Ceff < 40

0.83 (5/6)

1.2 (13/11)

Odds ratio, CMLE = 3.44 (0.83, 16.27) a

Data are expressed as an odds ratio using the conditional maximum likelihood estimator

(CMLE) with a 95% confidence interval.27 The lower 95% bound of 3.56 (total drug) for the odds ratio indicates that, given the same size and experimental variation, it is unlikely that the predicted ETS result is random (i.e., an odds ratio of one). The upper 95% bound is indicative of the high uncertainty of the predictive magnitude of the prediction rule. The applicability domain for this set of compounds has a reportable event rate of 78.6%.

Our current practice in generating TI values is to use the free plasma NOAEL concentration determined in ETS and the free plasma predicted human concentration (Ceff). The rationale for using free plasma drug levels derives from our belief in the free drug hypothesis, which states that only free drug concentration at the site of action exerts the biological response (efficacy and/or toxicity). Using free plasma levels as a surrogate for tissue exposure assumes 14 ACS Paragon Plus Environment

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that equilibrium between compartments (plasma versus tissues/organs) has been achieved. This assumption comes with caveats: for example, it is widely known that free brain levels do not equal free plasma concentrations for numerous compounds.28 This disequilibrium is likely to exist for other tissues as well, and will vary from organ to organ based on the ability of a compound to permeate through the membranes of organs, and/or the presence or absence of transporters, either uptake or efflux. If uptake transporters are present and a compound is transported into the organ, a high organ drug concentration is likely to be achieved. Perhaps this is the reason why Ceff (total drug) correlates to a higher degree with pass/fail rates than does free drug Ceff level (Figures 4A, 4B), in that the plasma total drug burden better represents the distribution of drug for all compartments. While higher specificity was achieved for Ceff total than for Ceff free (0.96 versus 0.78), ROC AUC values for both were similar (0.77, 0.71, respectively), suggesting that both should be taken into account when considering the best compound to advance into an ETS. There has been an explosion in the development and commercialization of in vitro safety assays and computational tools to predict toxicity.29-33 While these assays and tools may be valuable in early compound triage, a better understanding of how they relate and translate to in vivo toxicology outcomes is desirable. Building the concordance of the measured in vitro endpoints from these assays with in vivo outcomes would be highly advantageous, yet has not been robustly demonstrated in the literature. Future work linking in vitro with in vivo data and thereby enabling TI prediction may result in improved compound triage and increase the odds of success. Safety outcomes defined by TI provide a rigorous way to evaluate safety data. Looking forward, this approach should be extended to in vitro cell-based assays to enable the in vitro data to be put into perspective. Additional evaluation using a TI-based ranking approach for ETS compound selection based on in vitro high-content mechanistic screens and in vivo zebrafish 15 ACS Paragon Plus Environment


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endpoints34 might provide earlier, less expensive, endpoints with which to triage compounds and reduce the large animal usage currently required for drug development.

Methods Data Collection ETS compound set. The ETS compound set used in this study includes 56 compounds from 15 different MOAs. All compounds were designed to be orally available and CNSpermeable, thus both peripheral and central exposure is expected to be achieved. All compounds in this study were considered to be suitable compounds to advance to the clinic if they passed regulatory toxicology studies. The list of target families and number of candidates for each target family for the ETS set appears in Figure 1. The data used for this analysis can be found in the supplementary material (Table 1s). The MOA in vitro assay endpoints (Ki, IC50, EC50, Kinact/Ki) utilized in the in vitro potency, LE, LipE and LELP analysis were the primary screening assays for each MOA; these values were extracted from the company file.

Various

ex-vivo and in vivo methods, including but not limited to: behavioural, electrophysiology and microdialysis experiments were utilized to establish Ceff for each of the compounds. Predicted human Ceff was typically Css, ave 0-24h required to achieve the desired biological response effects. Every effort was made to accurately capture predicted human efficacious concentration as utilized by the project teams to make decisions, including TI calculations. Rat, dog and monkey free efficacious drug concentrations were scaled using the appropriate, experimentally derived, plasma protein binding values. Safety Studies. Exploratory toxicology studies (ETS) up to 3 weeks in duration were completed in at least one species selected from Sprague Dawley or Wistar rats, beagle dogs, and cynomolgus monkeys. Drug formulations were typically prepared in 0.5% methyl cellulose and administered at a dosage volume of 10 mL/kg in rats and 1 mL/kg in dogs and monkeys. 16 ACS Paragon Plus Environment

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Monitored endpoints in these studies included clinical signs (daily), food intake, body weight, clinical pathology (hematology, serum chemistry; end of study), vital signs where warranted (heart rate, respiratory rate; dogs, predose and postdose at about Tmax at end of study), electrocardiogram (dogs, predose and postdose at about Tmax at end of study), plasma drug levels (day 1 and/or end of study; 4-5 time points on each sampling day), and gross histopathology (end of dosing phase in repeat dose studies). All procedures performed on these animals were in accordance with regulations and established guidelines and were reviewed and approved by an Institutional Animal Care and Use Committee or through an ethical review process. Safety margins derived from ETS in rats (first species), dogs or monkeys (second species) are summarized in Figure 7. For this analysis a general ≥30x NOAEL TI pass/fail guideline was used (Cmax for seizures and Cave for histopathology). Within all therapeutic areas, acceptable TIs vary from target to target depending on the intended patient population and type of toxicity observed in the ETS. Twelve compounds within our analysis did not achieve the 30x TI but were considered for continued development; Cmax-driven toxicities (e.g., convulsion) are the primary finding that allow a pass in the absence of a 30-fold index, but only when no AUC-related toxicity is noted and the AUC TI is >30x. One other exception to the 30x TI pass/fail criterion occurs when the toxicity is not-life threatening, and is deemed to be both monitorable and manageable in the clinical setting. Meeting a TI of 30 with no findings in these cases is not necessary for a molecule to advance into preclinical development.



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Figure 7. Distribution of exploratory toxicology compounds against ETS TI [NOAEL / Ceff (free drug)]. Compounds are colored by ETS outcome: compounds that passed ETS are in green and failed compounds are in red. Vertical lines indicate the targeted TI of 30.

Physicochemical Properties and Data Analysis. The physical chemical properties of the safety compound set were calculated and determined to be diverse across six standard physical chemical property measures (ClogP, ClogD, TPSA, MW, HBD, pKa) (Figure 8). Further, the chemical space that these compounds represent is general and covers drug-like propertiy space.14 Good overlap of the physicochemical properties was found in a comparison of the safety compound set and a randomly generated subset of compounds from our cooperate file, Figure 3s. For the work herein, calculated CNS MPO desirability scores were obtained using the published algorithm,15 and calculated physicochemical properties were obtained using standard commercial packages: Biobyte for ClogP calculations; for calculation of TPSA, see Ertl.35 Statistical analyses were carried out using SAS JMP 7 statistical software,36 and the data was 18 ACS Paragon Plus Environment

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visualized with JMP or Spotfire DXP.37 The R programming language (version 3.0.0) was used along with version 1.5.4 of the pROC package. Logistic regression was used to model the probability of success.26 Models were assessed for nonlinear relationships between the Ceff data and ETS success using restricted cubic splines. In each case, formal statistical tests (as well as visual inspection of the fitted curves) indicated that nonlinear effects were not supported by the data.

Figure 8. Physicochemical property distribution and statistics of the safety compound set are shown for ClogP, ClogD, MW, TPSA, HBD, and pKa. N represents the number of compounds included in each analysis.

ADME Data. Data on the following in vitro ADME properties were taken from the company files. All assays were performed via reported methods as described previously for: (a) passive apparent permeability, Papp, assay,38 (b) P-glycoprotein (P-gp) efflux liability assay,38 (c) metabolic stability, expressed as unbound intrinsic clearance (CLint,u),39, 40 and (d) plasma protein binding, Fu for rat, dog, monkey and human.



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Supporting Information The following safety compound set data in tabulated form: MOA, ETS definitive outcome, biological target, Ceff (total drug), Ceff (free drug), rat Ceff, 2nd species Ceff, in vitro potency, LE, LipE, LELP, 1st species, rat NOAEL, rat NOAEL Cmax, Rat NOAEL Cave, TI Rat NOAEL, rat outcome, 2st species, 2st species NOAEL, 2st species NOAEL Cmax, 2st species NOAEL Cave, TI 2st species NOAEL, 2st species outcome, hFu, rFu, 2nd species Fu, Clint,u, Papp, P-gp, calculated microsomal protein binding, drug property alignment, ClogP, TPSA, CNS MPO desirability. Pie chart of binned values for alignment of desired ADME attributes. Distribution of exploratory toxicology compounds: CNS MPO desirability score versus Ceff (total drug) . Distribution of exploratory toxicology compounds and a Pfizer diversity compound set. This information is available free of charge via the Internet at http://pubs.acs.org. Acknowledgements. The authors thank Editors-in-Chief Drs. Wang and Georg for granting a waiver on data deposition. The authors thank Anabella Villalobos, Patrick Verhoest and the entire Pfizer Neuroscience department for helpful discussions in the development of this manuscript. The authors thank Katherine Brighty for her insightful comments on and review of this manuscript. Corresponding Author *To whom correspondence should be addressed. Mailing address: Pfizer Worldwide Research and Development, 700 Main Street, Cambridge, MA 02139; Tel: 857-225-2840; Fax 860-6866052; E-mail: [email protected] Abbreviations Used ADME, absorption, distribution, metabolism and excretion AUC, area under the curve Cave, average concentration 20 ACS Paragon Plus Environment

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Ceff, efficacious concentration Cmax, maximum concentration CNS, central nervous system ETS, exploratory toxicology study GPCR, G protein-coupled receptor HBD, hydrogen bond donor HLM, human liver microsomes IND, Investigational New Drug application LE, ligand efficiency LELP, the ratio of log P and ligand efficiency LipE, lipophilic efficiency MDR, multi-drug resistance MPO, multi-parameter optimization MM, molecular mass NOAEL, No Observed Adverse Effect Level P-gp, P-glycoprotein ROC, receiver operating characteristic SAR, structure-activity relationship TI, therapeutic index TPSA, topological polar surface area.

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Table of Contents Graphic


Page 27 of 39


Exploratory Toxicology Study Outcome 11 1 10 Fail 2 3 Pass 4 5 6 6 6 7 5 4 8 9 10 11 12 4 13 1 14 3 15 3 3 16 2 1 3 2 17 18 19 2 2 20 1 1 21 1 1 1 1 1 1 22 23 24 25 MOA 1 2 3 4 5 6 7 8 9 26 27 28 Kinase Phosphodiesterase Proteases Transferase Aminergic Metabotropic 29 30 31 Enzyme GPCR 32 33 34 35 36 37 38 39 ACS Paragon Plus Environment 40 41 42

Figure 1.

4 1 3 2 2 2

3

4 3

3 2

2 2 1

10

11

12

Peptide

Ligand-Gated

Ion Channel

13 VoltageGated

1

1

14

15

Transporter + Aminergic Transporter and GPCR


ETS Outcome:

Fail

Pass

Pass/Fail (6/15)

Pass/Fail (3/1)

Pass/Fail (7/7)

Pass/Fail (7/10)

5

4

3 ClogP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

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2

1

0

-1

-2

Figure 2.

20

30

40

50

60

70

75

TPSA ACS Paragon Plus Environment

80

90

100

Page 29 of 39 A)

Journal of MedicinalB) Chemistry

MOA

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

MOA

Better In Vitro potency

Better LE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1

10

100

1000

10000

0.3

0.35

0.4

In vitro potency nM (Ki, EC50, Kinact/Ki,IC50)

0.45

0.5

0.55

LE

C)

D) MOA

MOA

Better LipE

Better LELP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 3.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 2

3

4

5

6

7

8

9

LipE

0

5

10 LELP

ACS Paragon Plus Environment

15

0.6

A)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

MOA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

ETS Outcome:

Fail

Journal of Medicinal Chemistry Pass 250 nM (total drug)

PageC30 Lowest eff of 39 passes ETS

20

36

1 14

19

22 Ceff ≤ 250 nM (total drug)

Ceff >250 nM (total drug)

1

10

100 Ceff (total drug, nM)

1000

10000

B) 40 nM (free drug)

35 MOA 1 2 17 18 3 4 Ceff ≤ 40 nM 5 (free drug) 6 7 8 9 10 11 12 13 14 15

Figure 4.

21 5 16 Ceff > 40 nM (free drug)

0.1

1

10 ACS Paragon Environment Ceff (freePlus drug, nM)

Yes No Yes n/a Yes Yes n/a Yes Yes n/a No n/a Yes Yes No

100

Lowest Ceff passes ETS

Yes No Yes n/a No Yes n/a Yes Yes n/a No n/a Yes Yes Yes

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B) Ceff (free drug)

Ceff (free drug)

Ceff (total drug)

1.0

Ceff (total drug)

1 nM (>0.8)

0.8

250 nM (0.96, 0.58)

16 nM (0.74, 0.73)

0.6

40 nM (0.78, 0.48)

0.4

ROC AUC Ceff (free drug) = 0.711 Ceff (total drug) = 0.775

0.2

Probability of Success

0.8 119 nM (0.78, 0.70)

Increasing Probability Success

Minimize Failures Sensitivity

1 2A) 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42


0.6

0.1 nM (>0.7)

10 nM (>0.6)

1 nM (>0.5) 100 nM (>0.4)

0.4

10 nM (>0.4) 1000 nM (0.2)

100 nM (>0.2)

0.2

0.0 1.0

Figure 5.

0.8

0.6 0.4 Specificity

0.2

0.0

0.1

1

10

100

1000

Drug Concentration (nM) Reduce Ceff – increase success

Maximize Passes


10000


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Fail ETS

Pass ETS

1

15

1

1 7

14

1 5

2

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1

Figure 6. ACS Paragon Plus Environment

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One Attribute CNS MPO ADME Ceff Two Attributes CNS MPO, ADME CNS MPO, Ceff ADME, Ceff Three Attributes CNS MPO, ADME, Ceff

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Table 1.


Total Drug ETS Outcome (Pass/Fail) CNS MPO < 4 CNS MPO > 4

Free Drug ETS Outcome (Pass/Fail) CNS MPO < 4 CNS MPO > 4

Ceff >250

NA (0/2)

0.059 (1/17)

Ceff >40

NA (0/1)

0.33 (5/15)

Ceff ≤ 250

1 (5/5)

1.9 (17/9)

Ceff < 40

0.83 (5/6)

1.2 (13/11)

Odds ratio, CMLE = 29.5 (3.56, 1410.00)a

Odds ratio, CMLE = 3.44 (0.83, 16.27) a


Journal of Medicinal Chemistry A)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

B)

MOA

TI = 30, Cmax

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

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MOA

TI = 30,Cave

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1

10

100 1000 10000 TI Rat NOAEL Cmax /Ceff

100000

C)

0.1

1

10 100 TI Rat NOAEL Cave 0-24 /Ceff

1000

10000

D) TI = 30, Cmax

MOA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 7.

TI = 30, Cave

MOA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1

10 100 1000 10000 TI 2nd Species NOAEL Cmax /Ceff

100000


0.1

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10000

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Figure 8.




1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Aligned ADME Properties (Papp, P-gp, and CLint,u) 2 (18.2 %) 0/3 Attributes 1/3 Attributes 2/3 Attributes 3/3 Attributes 2 (18.2 %)

11

Figure 1s.

19

22 1 (5.3 %) 3 (15.8 %)

4 (36.4 %)

3 (27.3 %) CNS MPO Desirability Score

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2 250 nM

total drug

Graphical Abstract

17 18

19

Ceff

≤ 40 nM

5 16

> 40 nM

free drug


Improving the Odds of Success in Drug Discovery - American

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