Derivative of Irregularly Sampled Noisy Data with Savitzky-Golay Filter

I'm trying to find the derivative of some data that is both irregularly sampled, and somewhat noisy. It should be monotonically decreasing in theory. I've attached a file with the data for reference. Is there a way, SG or otherwise, that it would be possible to get a derivative of the relatively smooth input data that is smoother than this? Especially near the begining and end.

Modifying the SG parameters only does so much, (2,41) seems okay, but I start to lose info at the start. Is there another way around this? Also possibly with better resampling method?

clc;clear all;close all

%Load data
data=load('data.mat');t=data.M(:,1);V=data.M(:,2);

%resample data
desired_Freq_samples = 1/5;
[V_r, t_r] = resample(V,t,desired_Freq_samples);

%SG smoothing and derivative
[b,g] = sgolay(5,25);
dt = t_r(2)-t_r(1);
dx = zeros(length(V_r),2);
for p = 0:1
  dx(:,p+1) = conv(V_r, factorial(p)/(-dt)^p * g(:,p+1), 'same');
end

%plot
s1=subplot(2,1,1);plot(s1,t,V,'--.',t_r,V_r,'.-',t_r,dx(:,1))
legend(s1,'V','V (resampled)','V (smoothed)','location','best')
s1.XLim=[min(t)-10 max(t)+10];

s2=subplot(2,1,2);plot(s2,t_r,dx(:,2))
legend(s2,'V''','location','best')
s2.YLim=[-4e-3 0];
s2.XLim=[min(t)-10 max(t)+10];