A sigmoidal dose-response curve that plots biological response against drug concentration on a logarithmic x-axis, fitted using a four-parameter logistic (4PL) model. This visualization is essential for determining drug potency metrics such as EC50 (half-maximal effective concentration) or IC50 (half-maximal inhibitory concentration), Hill slope steepness, and upper/lower response asymptotes. It enables rapid visual comparison of compound efficacy and is a standard tool in pharmacological analysis.
# anyplot.ai
# curve-dose-response: Pharmacological Dose-Response Curve
# Library: makie 0.22.10 | Julia 1.11.9
# Quality: 90/100 | Created: 2026-06-24
using CairoMakie
using Colors
using Random
Random.seed!(42)
# Theme tokens
const THEME = get(ENV, "ANYPLOT_THEME", "light")
const PAGE_BG = THEME == "light" ? colorant"#FAF8F1" : colorant"#1A1A17"
const ELEVATED_BG = THEME == "light" ? colorant"#FFFDF6" : colorant"#242420"
const INK = THEME == "light" ? colorant"#1A1A17" : colorant"#F0EFE8"
const INK_SOFT = THEME == "light" ? colorant"#4A4A44" : colorant"#B8B7B0"
const IMPRINT_PALETTE = [
colorant"#009E73",
colorant"#C475FD",
colorant"#4467A3",
colorant"#BD8233",
colorant"#AE3030",
colorant"#2ABCCD",
colorant"#954477",
colorant"#99B314",
]
# 4PL sigmoid: Bottom + (Top - Bottom) / (1 + (EC50/x)^hill)
function four_pl(x, bottom, top, ec50, hill)
return bottom + (top - bottom) / (1.0 + (ec50 / x)^hill)
end
# Compound A — potent, steep sigmoid (EC50 = 50 nM)
const BOTTOM_A = 2.0
const TOP_A = 97.0
const EC50_A = 50e-9
const HILL_A = 1.8
# Compound B — moderate potency, shallower (EC50 = 500 nM)
const BOTTOM_B = 3.0
const TOP_B = 91.0
const EC50_B = 500e-9
const HILL_B = 1.2
# Experimental data: 9 concentration points from 1e-9 to 1e-4 M
conc_data = exp10.(range(-9.0, -4.0, length=9))
log_conc_data = log10.(conc_data)
response_a = clamp.([four_pl(c, BOTTOM_A, TOP_A, EC50_A, HILL_A) + 5.0 * randn() for c in conc_data], 0.0, 100.0)
sem_a = 2.0 .+ rand(9) .* 2.0
response_b = clamp.([four_pl(c, BOTTOM_B, TOP_B, EC50_B, HILL_B) + 5.0 * randn() for c in conc_data], 0.0, 100.0)
sem_b = 2.0 .+ rand(9) .* 2.0
# Smooth fitted curves (300 points for visual smoothness)
conc_fit = exp10.(range(-9.0, -4.0, length=300))
log_conc_fit = log10.(conc_fit)
curve_a = [four_pl(c, BOTTOM_A, TOP_A, EC50_A, HILL_A) for c in conc_fit]
curve_b = [four_pl(c, BOTTOM_B, TOP_B, EC50_B, HILL_B) for c in conc_fit]
# 95% CI band for Compound A (approximate fixed half-width)
ci_upper_a = clamp.(curve_a .+ 7.5, 0.0, 100.0)
ci_lower_a = clamp.(curve_a .- 7.5, 0.0, 100.0)
# EC50 reference positions
log_ec50_a = log10(EC50_A)
log_ec50_b = log10(EC50_B)
half_max_a = BOTTOM_A + (TOP_A - BOTTOM_A) / 2.0
half_max_b = BOTTOM_B + (TOP_B - BOTTOM_B) / 2.0
# Title font sizing — scale down for long titles
title_str = "Drug Potency Comparison · curve-dose-response · julia · makie · anyplot.ai"
titlesize = max(13, round(Int, 20 * min(1.0, 67 / length(title_str))))
# Figure
fig = Figure(
resolution = (1600, 900),
fontsize = 14,
backgroundcolor = PAGE_BG,
)
ax = Axis(
fig[1, 1];
title = title_str,
titlesize = titlesize,
titlecolor = INK,
xlabel = "Concentration (M)",
ylabel = "Response (%)",
xlabelsize = 14,
ylabelsize = 14,
xlabelcolor = INK,
ylabelcolor = INK,
xticklabelsize = 12,
yticklabelsize = 12,
xticklabelcolor = INK_SOFT,
yticklabelcolor = INK_SOFT,
xtickcolor = INK_SOFT,
ytickcolor = INK_SOFT,
backgroundcolor = PAGE_BG,
topspinevisible = false,
rightspinevisible = false,
leftspinecolor = INK_SOFT,
bottomspinecolor = INK_SOFT,
xgridcolor = RGBAf(INK.r, INK.g, INK.b, 0.12),
ygridcolor = RGBAf(INK.r, INK.g, INK.b, 0.12),
xminorgridvisible = false,
yminorgridvisible = false,
xticks = ([-9.0, -8.0, -7.0, -6.0, -5.0, -4.0], ["10⁻⁹", "10⁻⁸", "10⁻⁷", "10⁻⁶", "10⁻⁵", "10⁻⁴"]),
)
xlims!(ax, -9.5, -3.6)
ylims!(ax, -6.0, 108.0)
# Asymptote reference lines (top and bottom plateaus)
hlines!(ax, [TOP_A, BOTTOM_A]; color = (IMPRINT_PALETTE[1], 0.25), linestyle = :dash, linewidth = 1.2)
hlines!(ax, [TOP_B, BOTTOM_B]; color = (IMPRINT_PALETTE[2], 0.25), linestyle = :dash, linewidth = 1.2)
# 95% CI band for Compound A
band!(ax, log_conc_fit, ci_lower_a, ci_upper_a; color = (IMPRINT_PALETTE[1], 0.15))
# Fitted sigmoid curves
lines!(ax, log_conc_fit, curve_a;
color = IMPRINT_PALETTE[1], linewidth = 2.5,
label = "Compound α (EC50 = 50 nM)")
lines!(ax, log_conc_fit, curve_b;
color = IMPRINT_PALETTE[2], linewidth = 2.5,
label = "Compound β (EC50 = 500 nM)")
# EC50 reference lines — vertical and horizontal dashed
vlines!(ax, [log_ec50_a]; color = (IMPRINT_PALETTE[1], 0.55), linestyle = :dash, linewidth = 1.5)
vlines!(ax, [log_ec50_b]; color = (IMPRINT_PALETTE[2], 0.55), linestyle = :dash, linewidth = 1.5)
hlines!(ax, [half_max_a]; color = (IMPRINT_PALETTE[1], 0.55), linestyle = :dash, linewidth = 1.5)
hlines!(ax, [half_max_b]; color = (IMPRINT_PALETTE[2], 0.55), linestyle = :dash, linewidth = 1.5)
# Error bars and experimental data markers
errorbars!(ax, log_conc_data, response_a, sem_a;
color = IMPRINT_PALETTE[1], linewidth = 1.5, whiskerwidth = 8)
scatter!(ax, log_conc_data, response_a;
color = IMPRINT_PALETTE[1], markersize = 10,
strokewidth = 1.0, strokecolor = PAGE_BG)
errorbars!(ax, log_conc_data, response_b, sem_b;
color = IMPRINT_PALETTE[2], linewidth = 1.5, whiskerwidth = 8)
scatter!(ax, log_conc_data, response_b;
color = IMPRINT_PALETTE[2], markersize = 10,
strokewidth = 1.0, strokecolor = PAGE_BG)
# Legend (upper-left)
axislegend(ax;
position = :lt,
backgroundcolor = ELEVATED_BG,
labelcolor = INK,
framecolor = INK_SOFT,
framewidth = 0.5,
padding = (8, 8, 8, 8),
rowgap = 4,
)
save("plot-$(THEME).png", fig; px_per_unit = 2)
Part of Pharmacological Dose-Response Curve on anyplot.ai.