Abstract
In cancer pharmacology (and many other areas), most doseresponse curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of inflection, or the presence of combined agonist and antagonist effects, prevents straightforward modelling of the data via a standard Hill equation. Here we propose a modified model and automated fitting procedure to describe doseresponse curves with multiphasic features. The resulting general model enables interpreting each phase of the doseresponse as an independent dosedependent process. We developed an algorithm which automatically generates and ranks doseresponse models with varying degrees of multiphasic features. The algorithm was implemented in new freely available Dr Fit software (sourceforge.net/projects/drfit/). We show how our approach is successful in describing doseresponse curves with multiphasic features. Additionally, we analysed a large cancer cell viability screen involving 11650 doseresponse curves. Based on our algorithm, we found that 28% of cases were better described by a multiphasic model than by the Hill model. We thus provide a robust approach to fit doseresponse curves with various degrees of complexity, which, together with the provided software implementation, should enable a wide audience to easily process their own data.
Introduction
Measuring drug effects on biological systems is part of many scientists’ routine^{1,2}. Observed effects span from the inhibition or agonism of proteins and other molecules^{3,4} to effects measured at the cell^{5}, tissue^{6} or whole organism levels^{7,8}. In cancer research, cell proliferation and viability are often assessed in a panel of cell lines specific to a given type of cancer^{9}. Typically, the biologist or pharmacologist compares populations of treated vs. untreated cells at various drug concentrations. The data is summarized via a dose response curve and then fitted using an inhouse program or commercial software. The fitted curve gives a mathematical description of measured effects and enables interpolating or extrapolating missing information. When various cell lines or drugs are also investigated, the resulting models facilitate comparing doseresponses by summarizing them via a few parameters^{10} (e.g the relative 50% effective concentration EC_{50}).
The fitting procedure that follows data acquisition can be challenging from several point of views. First of all, for most experimentalists who are not familiar with modelling, this procedure will require the availability of friendly software to fit the data. Moreover, most available programs will attempt to fit the data to a standard Hill model (also called 4 parameters logistic^{11}). The Hill model has been used extensively in the past. It can describe chemical reactions in mechanistic terms^{12} and enables excellent modelling of most cases. However, the Hill model is based on a unique point of inflection and cannot faithfully describe cases where agonist (stimulatory or hormetic) effects are also observed^{13,14}. Moreover, even in the absence of agonist effects, cases where there is more than one point of inflection in the inhibitory phase cannot be handled^{15}. This can result in poorly fitted curves which can mislead data interpretation, comparison to other cases, and extrapolation if such fits are accepted. Recently, HaibeKains et al.^{16} identified the choice of an estimator for summarising drug doseresponse curves as one of the reasons for lack of correlation of drug sensitivity in large pharmacogenomics screens^{17,18}. At the same time, the importance and implications of multiphasic doseresponses have been highlighted in various contexts^{19,20} including cancer therapeutics^{21,22}.
Extensions of the Hill model for asymmetrical curves have been developed, but these do not accommodate several points of inflection^{23,24,25,26}. Alternatives have been suggested to handle biphasic doseresponse curves^{27,28}. These were tailored to the specific case of an initial stimulatory effect and are not necessarily equipped with a common structure and straightforward interpretation of the resulting model. There have also been a number of efforts in improving doseresponse fitting^{29,30} and several free software products are available to fit doseresponse curves^{31,32,33,34}. These efforts often focused on the standard Hill model and did not provide a free tool which can be used to generate multiphasic doseresponse models in a userfriendly manner as per wellknown commercial packages^{35,36,37}. Note that there also exists general free language and environment such as R^{38} which easily enable cubic splines fitting (splines are curves generates by connecting polynomials). Nevertheless, splines fitting is not optimal due to the lack of model structure which affects the goodness of fit and prevents interpretation in mechanistic terms.
Here we present a general model which enables excellent fitting of doseresponse curves with multiphasic features. From a theoretical point of view, this new model combines dependent, cooperative effects as described by a Hill model, with independent effects as suggested by the Bliss approach for combination studies^{39}. We show that nonregular cases encountered in cancer pharmacology can be satisfactorily handled by this approach which combines these two classical pharmacological models. We have developed an algorithm which enables automated fitting of doseresponse curves and have implemented it in freely available software (Drfit as per Doseresponse Fitting). This approach was successful in modelling doseresponses which could not be described by a standard Hill equation. We then analysed a large screen involving 11650 doseresponse curves and found that a substantial proportion of cases were better described by this approach.
Results
From Hill to multiphasic models
The Hill model is based on the following equation which describes the effect E obtained at a given concentration C:
where EC_{50} is the relative 50% effective concentration, H is the hill exponent, E_{∞} is the maximum effect and E_{0} is the effect in the absence of drug. This equation can also be manipulated and written under alternative forms or via different definitions of its parameters. If the dose response is built by considering a measure of the system being studied (e.g. amount of cells or of proteins) in treated conditions over this same measure in untreated conditions, then the baseline value E_{0} is fixed to unity (the doseresponse can also be expressed in terms of percentage as it is done here). Figure 1a shows the typical sigmoidal curve that is obtained with the Hill model. The figure also shows that varying the EC_{50} shifts the curve in logspace while varying the E_{∞} changes the effect level obtained at high concentrations (Fig. 1b). Finally, the hill exponent H can be used to account for various degrees of steepness (Fig. 1c). This model can therefore be used to fit typical doseresponse curves encountered in pharmacological studies (Fig. 1d).
In a significant number of cases, doseresponse curves show stimulatory effects (notably at low concentration; Fig. 2a), or two point of inflections (Fig. 2b), or even a combination of these features (Fig. 2c). In these cases, it is obvious that attempting to fit the data to a Hill model cannot result in a satisfactory description of the data (red lines in Fig. 2a–c). Here we propose a modelling approach that is based on breaking down each one of the observed phases into independent, separate processes. Then each of these distinct processes is considered as the observed effect of closely related subprocesses. The mathematical formulation of this approach is as follows.
We first consider each phase separately and model it using a standard Hill model. For each phase i we write:
Then we consider each one of these phases as being part of successive reactions which independently converge toward the same phenotype, thus resulting in the total effect E:
where E(C) is the observed doseresponse curve, and E_{i}(C) is the dose response curve corresponding to the underlying ith independent process. Note the wellknown similarity between this formulation and the probability of independent events. This leads to the following model when considering all phases:
Note that this formulation is only correct when the response in the absence of drug is unity. Alternative formats can nevertheless be obtained by scaling this equation appropriately. Using this model with n = 2 two phases of inhibition (Fig. 3a) or one stimulatory and one inhibitory phase (Fig. 3b) can be described. Using n = 3, a multiphasic dose response with one stimulatory and two inhibitory phases can be described (Fig. 3c). There is no theoretical limit and for instance using n = 5, we can also describe more complex doseresponse curves such as one that involves two stimulatory and three inhibitory phases (Fig. 3d).
It is important to realize that the higher the number of process incorporated (i.e. the greater is n), the more it is difficult to experimentally discern the presence of each process due to inherent biological variation and experimental error. Also, the inclusion of each additional phase requires 3 parameters, so for n = 3 phases, 9 parameters are required. Therefore we compared the classical monophasic case (Hill equation) with three configurations only. These were cases where two stimulatory (n = 2), or one stimulatory and one inhibitory (n = 2), or one stimulatory and two inhibitory phases (n = 3) were present. In our experience, these configurations enable to describe most cases encountered in cancer pharmacology.
Automated curve fitting
We developed an optimization process to automatically generate and provide models with either one phase of inhibition, two phases of inhibition, one stimulatory and one inhibitory phase, or one stimulatory and two inhibitory phases. Our approach was based on an optimization algorithm and a ranking test^{40} which were both incorporated in the DrFit software (Fig. 4; http://sourceforge.net/projects/drfit/). Details of the overall method, including the formulation of the optimized objective function and how to use the software, can be found in the methods section.
Our approach was used to model the three examples of multiphasic doseresponse curves shown in Fig. 2 and resulted in excellent descriptions of the data (Fig. 5). Each case was classified as a monophasic or multiphasic doseresponse based on the Bayesian Information Criterion (BIC). The BIC criterion enables robust model ranking and selection and is based on a Bayesian approach^{40}. The BIC criterion was chosen here over other classical ranking methods because it penalizes overfitting more than other wellknown ranking criteria^{41,42,43}. Thus a conservative approach is used here where simpler models are favoured versus more complex multiphasic ones.
Using this approach, the first case was recognized as a 2 processes model involving a stimulatory and an inhibitory phase (Fig. 5a). The second case was recognized as a 2 processes model involving two inhibitory phases (Fig. 5b). The third case was recognized as a 3 processes model involving a stimulatory and two inhibitory phases (Fig. 5c). We then wanted to assess what proportion of cases might be better described by a multiphasic doseresponse curve rather than a standard Hill equation.
As an example, we analysed 11650 doseresponse experimental cases available from the Cancer Cell Line Encyclopedia (CCLE)^{17,44}. This dataset was generated using HighThroughput Screening (HTS) technology and its analysis presents several challenges. First, the very large amount of data precludes visual inspection of each case. Second, the noisy nature of HTS generated data complicates model identification. Our approach was successful in analysing this dataset and showed that (Fig. 6a), based on our BIC ranking test, overall 72% of cases could be explained using a monophasic standard Hill equation (e.g. Fig. 6b). Thus, a substantial proportion of doseresponses were found to be better described by a multiphasic approach (28%). Most of these were appropriately described by a biphasic model with an initial stimulatory effect (16%, e.g. Fig. 6c). A substantial number (8%) was appropriately described with two point of inflection (e.g. Fig. 6d) and only 4% required a triphasic model (e.g. Fig. 6e).
Discussion
Doseresponse curves are routinely generated in the laboratory environment to assess the effect of drugs and other agents on molecular, cellular and other models. In most cases, a single inflection doseresponse is observed which can be modelled using a classical Hill model. Nevertheless, there are instances where doseresponses show an initial stimulatory effect (also termed hormesis^{20}) or is fully inhibitory but with two point of inflections (biphasic). In a number of instances, a combination of these features can be observed. It is often desirable to model the observed doseresponse in order to obtain a mathematical description which can also be used to interpolate, extrapolate or derive metrics such as EC_{50} or maximum effects.
The challenge in identifying and modelling multiphasic doseresponse curves can be attributed to the increased complexity and decreased interpretability of potential approaches to model multiphasic doseresponses. Increased complexity is often associated to poor fitting or difficulty in generating appropriate fitting, particularly for experimentalists who are not necessarily familiar with these procedures. We have therefore developed a general model which we encapsulated in a fittingranking procedure and software, producing a robust, userfriendly tool.
Most advances in modelling doseresponse data have focused on hormetic effects and have been made in the fields of toxicology and environmental science^{28,45,46,47,48,49}. Our approach is based on simply combining the classical Hill and Bliss models and enables describing doseresponse data with complex multiphasic features. Thus we explain observable inflections as the result of perturbing underlying independent processes (Fig. 7). Each Hill model term in our formulation can be interpreted as the effect of the drug interacting with a series of converging, cooperative processes^{12} (e.g. interaction with a specific biological pathway). The product of these Hill model terms can then be interpreted in terms of perturbed processes (e.g. pathways) which are independent^{50}. Therefore our approach enables describing doseresponse data in a robust manner and it also offers interpretation in broad mechanistic terms.
It should be noted that doseresponse curves, including those with two or more inflections, can in theory be fitted using splines^{51,52} or other polynomialsbased approaches^{53,54}. The question therefore arises to why an alternative approach such as ours would be useful. We believe that the answer to this question is rooted in the same reasons which make the use of the classical Hill model popular for traditional monophasic doseresponse curves. The first reason is that the number of parameters used with splines fitting increases linearly with the number of knots (the points where the polynomial pieces connect). This adversely affects the goodness of fit as measured via a maximum likelihood approach. The second reason is that when using splines, the resulting doseresponse model is not indicative of potential underlying mechanisms. This is not the case in our approach where the resulting model structure indicates if there is a stimulatory effect or/and a second inhibitory phase (which could indicate interference with distinct processes). Also, the parameters do not correspond to specific features of the curve (steepness, half inhibitory concentration, maximum effect). In respect of all these points, our model is in line with the classical Hill model and can be viewed as an extension of it in the case of multiphasic cases.
We have also developed an algorithm which enables determination of how many processes can be observed (in order to tailor the model at the right level) based on statistical consideration. The resulting procedure has been integrated in our newly developed userfriendly DrFit software. We also proceeded to analyse a large dataset and showed that, based on models ranking, a substantial number of cases do not fit the Hill model and might require multiphasic models (28%). It should be noted that several authors already highlighted the importance and implications of multiphasic doseresponse such as hormetic effects^{19,20}. More recently, the relevance of complex doseresponse mechanisms has also been highlighted in the context of cancer therapeutics^{21,22}. Our finding that a substantial proportion of a large dataset is better described by a multiphasic model might suggest that these effects are not exceptionally rare.
From a practical point of view, this approach enables classifying observed doseresponse in a number of ways. It enables the user to distinguish between mono vs. multiphasic doseresponse. It also enables the user to distinguish between various types of multiphasic features (e.g. stimulatory + inhibitory phases vs. stimulatory + 2 inhibitory phases). Moreover, more quantitative comparisons across agents or systems can be performed using parameters such as the EC_{50} for each one of the identified phases. Parameters also provide mechanistic insight, for instance in terms of understanding at what concentration level a specific process is engaged (e.g. first inhibitory vs. second inhibitory phase).
Interpolation and extrapolation exercises are improved using this approach. This can have impact in a number of instances, such as for instance in the case of assessing drug combinations^{55}. It should also be noted that other important metrics such as the area under the curve (AUC) can be derived from the doseresponse model^{56}. For instance, the AUC together with the IC50 were considered to compare the results of two largescale pharmacogenomics studies^{16}. The calculation of such metrics should also benefit from our new approach.
The implementation of our general model and method in DrFit software should enable robust processing of both standard doseresponse curves and multiphasic ones. We expect DrFit to be a tool useful for many experimentalists and analysts interested in the study of agent effects on biological systems.
Material and Methods
Modelling
The model introduced in the Results section was implemented using Matlab. A three processes model was implemented using the following equation:
DrFit software reads the doseresponse data and builds the model. The algorithm implemented in DrFit first attempts to fit the data to a one process model (i.e. a standard Hill equation). This is followed by an attempt to fit the data to a two inhibitory processes model. Then Drfit attempts to fit the data to a two processes model which includes a stimulatory process. Finally, the software attempts to fit the data to a three processes model, two of them being inhibitory (antagonist) and one stimulatory (agonist). Three optimization algorithms were implemented to find the model parameters: the simplex constrained, the Trustregionreflective and the standard simplex. The Simplex constrained is the same algorithm as per the Simplex but constrains the parameters of the stimulatory and inhibitory processes such that no overlap in concentration space occurs (well separated processes). For all optimization algorithms, the following function F is minimized:
where c_{j} corresponds to the j^{th} concentration (p concentrations), E_{exp i}(c_{j}) corresponds to the measured effect for concentration level c_{j} and replicate i (n replicates), and σ_{j} is the standard deviation obtained for all the measures at concentration c_{j}. For models with only two processes EC_{50 3}, H_{3} and E_{∞3} were fixed to 1. For the model with only one process (i.e. a Hill model), EC_{50 2}, H_{2} and E_{∞2} were also fixed to 1.
For normally distributed observational noise, minimizing our function F corresponds to a maximum likelihood estimate of the parameters. Thus, when more than one replicate is available, goodness of fit (GOF) can be tested for and is also provided by the software. The resulting four models are then ranked based on the Bayesian Information Criterion (BIC) and the model with the lowest BIC score is proposed by the software as the best model^{40}. The BIC is our favourite ranking score because it favours simpler models, thus avoiding overparmetrization^{41,42,43} (the Akaike Information Criterion^{57} (AIC) score is also provided by the software if needed). In some instances, the absence of an appropriate number of replicates prevents satisfactory assessment of the standard deviation. In this case the software also enables weighting the sum of squares in the function F by unity rather than the standard deviation.
It is also important to note that, if we assume for instance that the doseresponse data is normalized to control conditions (no drug), then measured effects vary from 1 at low concentration to a value often close to 0 at high concentration. In practice, it is possible that fluctuations lead to a poor estimation of control conditions (i.e. not enough replicates, spatial effects in plated experiments etc.) such that the baseline may appear greater or lower than 1. Thus, the option is given to also scale the model in order to capture a different baseline and minimize these effects.
Software main features
DrFit software was developed to enable endusers to easily model their doseresponse data without requiring any coding or scripting. The software can be downloaded from sourceforge.net/projects/drfit and it is easily installed on windows64 machines. DrFit is accessible through an userfriendly interface (Fig. 4a). Experimental data is simply tabulated in a .xls file (Fig. 4b) and there are no limits in terms of concentration points and number of replicates (Fig. 4c; each replicate is simply added next to the previous one). The data for the software should be ordered as follows: concentrations, effects for replicate 1, effects for replicate 2 etc, then concentration unit and then title. The data can be ordered either in column or rows and several formats for the doseresponse can be selected (Effects varying from 1 to 0 or 100 to 0 or 0 to 1 etc). A template and several examples are provided online.
Once the data is loaded, it is displayed in the right panel of the software (Fig. 4d). The user can then proceed to automatically produce a model which will be also displayed in the right panel. Upon pressing the “Fit curve” button, DrFit proceeds to automatically fit the data (Fig. 4e). The plots can be further modified (Fig. 4f) and saved as high quality figures (Fig. 4g). The models’ characteristics (parameters, scaling, χ^{2}, GOF, AIC and BIC) can also be saved in a .xls file in the project’s folder (Fig. 4h). Additionally, effects can be interpolated or extrapolated at various concentrations (Fig. 4i) and effective concentration at 50% (i.e. EC_{50} of the overall) or at any other level (e.g. EC_{90}) can be calculated via DrFit interface (Fig. 4j). Several doseresponse curves can be displayed in the right panel (with same or different colour) and the whole figure saved with high resolution.
Cells and reagents
The KPC K8484 cancer cell line was established from a KPC PDAC tumour^{58}. PANC1 cells were obtained from the European Collection of Cell Cultures (ECACC; Health Protection Agency, Salisbury, UK) and were verified by STR genotyping and tested negative for mycoplasma. Cells were grown in DMEM medium (Life Technologies) supplemented with 5% fetal calf serum at 37 °C and 5% CO2. SKOV3 cells were obtained from ATCC and tested negative for mycoplasma. They were grown in RPMI medium (Life Technologies) supplemented with 5% fetal calf serum at 37 °C and 5% CO2. Gemcitabine was obtained from Tocris Bioscience (Bristol, UK).
Gemcitabine was dissolved in dimethylsulphoxide (DMSO) and then diluted in culture medium to a final concentration of 0.2% DMSO. PF477736 was obtained from SynKinase (Parkville, Austrailia), dissolved in DMSO at 10 mM, and serially diluted to the appropriate concentration.
Cytotoxicity assay
For K8484 and PANC1, drug cytotoxicity in vitro was assessed by the means of Sulforhodamine B colorimetric (SRB) assay. Cells were plated with a range of concentrations. After 72 h of incubation at 37 °C, they were fixed (3% trichloroacetic acid in water (w/v), 90 minutes, 4 °C), washed in water and stained with a 0.057% SRB (Sigma) solution in acetic acid (w/v) for 30 minutes. The plates were washed (1% acetic acid (v/v), 4 times), and the proteinbound dye was dissolved in a 10 mM Tris base solution (pH 10.5). Fluorescence was measured using Tecan Infinite M200 platereader (excitation 488 nm, emission 585 nm). Percentage inhibition compared to solvent controltreated cells was calculated for each drug concentration.
For SKOV3, drug toxicity was assessed by cell titer glo. Approximately 500 cells per well in 5 uL was dispensed using a Multidrop Combi dispenser (Thermo Fisher Scientific) into 1,536 solidbottom white Greiner Bioone tissue culturetreated plates (catalog #789173F). 23 nL of ). PF477736 was transferred to the assay plate using a Kalypsis pintool. Plates were covered with stainless steel cell culture lids and incubated at standard conditions for 48 hours. To assess viability, 3 uL of CellTiter Glo luminescent cell viability assay reagent (Promega) was added using a Bioraptor Flying Reagent Dispenser (Aurora DiscoveryBD). The plates were incubated for 15 minutes at room temperature and measured using a 10s exposure on a ViewLux (PerkinElmer).
Additional Information
How to cite this article: Di Veroli, G.Y. et al. An automated fitting procedure and software for doseresponse curves with multiphasic features. Sci. Rep. 5, 14701; doi: 10.1038/srep14701 (2015).
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Acknowledgements
This work was funded by Cancer Research UK grant C14303/A17197.
Author information
Affiliations
CRUK Cambridge Institute, University of Cambridge, UK
 Giovanni Y. Di Veroli
 , Chiara Fornari
 , Ian Goldlust
 , Graham Mills
 , Siang Boon Koh
 , Jo L Bramhall
 , Frances M. Richards
 & Duncan I. Jodrell
NIH Chemical Genomics Center, National Institutes of Health, Bethesda, USA
 Ian Goldlust
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Contributions
G.Y.D. has conceived this study and performed mathematical modelling and data analysis. G.Y.D. and C.F. developed the software. I.G., G.M., S.B.K. and J.L.B. performed the experiments. G.Y.D. drafted the manuscript. D.I.J. and F.R. supervised the laboratory. G.Y.D., D.I.J., F.R. and C.F. critically revised the manuscript.
Competing interests
The authors declare no competing financial interests.
Corresponding author
Correspondence to Giovanni Y. Di Veroli.
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